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Законы масштабирования дистилляции

Рекомендация для читателей:


Прежде чем погрузиться в детали, советую ознакомиться с двумя отличными статьями инженера из Яндекса (статья 1, статья 2). В них отлично объясняются принципы дистилляции, её применение в промышленных задачах и ключевые практические аспекты. Это идеальный старт для тех, кто только начинает знакомиться с темой.

Однако, если вы, как и я, стремитесь к глубокому пониманию — этого может оказаться недостаточно. В данном обзоре мы пойдём дальше:

  1. Математическая формализация: Разберём более глубако уравнения, лежащие в основе дистилляции, включая функцию потерь с температурным параметром, оптимизацию распределений и законы масштабирования из работы Apple.

  2. Примеры кода: Покажем, как реализовать дистилляцию на практике — от простых моделей на PyTorch до тонкой настройки гиперпараметров.

  3. Нюансы исследований: Ответим на вопросы, оставшиеся за рамками вводных материалов. Например, почему «слишком умный учитель» вредит ученику и как математически обосновать оптимальное соотношение их размеров.

Для кого это?
Если вы хотите не просто использовать дистилляцию «из коробки», а понимать, как и почему она работает — этот разбор для вас. Мы заглянем «под капот» методов, чтобы вы могли осознанно применять их в своих проектах.

Part 1: Knowledge Distillation

Knowledge Distillation (Дистилляция знаний) — это метод обучения моделей-студентов (обычно меньшего размера и менее сложных) путем передачи "знаний" от предварительно обученной модели-учителя (обычно большей и более сложной). Основная идея заключается в том, что модель-учитель, обладающая большей емкостью и обученная на большом объеме данных, может передать не только свои "жесткие" предсказания (например, класс объекта), но и более богатую информацию о распределении вероятностей классов, которую модель-студент может использовать для более эффективного обучения.

Teacher и Student модели:

В парадигме Knowledge Distillation участвуют две основные модели:

  • Teacher (Учитель): Это большая, предварительно обученная модель, которая считается "экспертом" в решении определенной задачи. Учитель уже достиг высокой точности и обладает "знаниями", которые мы хотим передать студенту. Математически учитель представляется как функцияp(y∣x), которая для входных данных xx выдает распределение вероятностей pпо классам y.

  • Student (Студент): Это меньшая, более простая модель, которую мы хотим обучить. Цель студента — научиться имитировать поведение учителя, чтобы достичь сравнимой производительности, но при этом быть более эффективной с точки зрения вычислительных ресурсов, памяти или времени инференса. Студент представляется как функция q_θ​(y∣x), где θ— параметры модели, которые мы оптимизируем в процессе обучения.

Функция потерь (Loss Function) в Knowledge Distillation:

Общая цель Knowledge Distillation — минимизировать разницу между предсказаниями учителя и студента. Это формализуется через функцию потерьL, которая зависит от предсказаний учителяp(y|x) и студента q_{\theta}(y|x). Процесс обучения заключается в поиске оптимальных параметров $\theta$ для студента, которые минимизируют эту функцию потерь:

L(p(y|x), q_{\theta}(y|x)) \rightarrow \min_{\theta}

Это общее выражение, и конкретный вид функции потерь и способ дистилляции определяют различные подходы. Рассмотрим два основных подхода: hard-label и soft-label дистилляцию.

Это общее выражение, и конкретный вид функции потерь и способ дистилляции определяют различные подходы. Рассмотрим два основных подхода: hard-label и soft-label дистилляцию.

Hard-label Distillation для GPT моделей: объяснение на пальцах

Представьте, что у нас есть две модели:

  • Учитель (Teacher): Большая, мощная GPT модель, например, GPT-3 или что-то подобное. Она обладает огромным количеством знаний о языке и мире, и способна генерировать очень качественный и связный текст.

  • Студент (Student): Маленькая, более компактная GPT модель, например, уменьшенная версия GPT или Transformer меньшего размера. Она менее ресурсоемкая, но изначально уступает учителю в качестве генерации текста.

Наша цель - "научить" маленькую модель-студента генерировать текст так же хорошо, как и большая модель-учитель, используя метод Hard-label Distillation.

Шаги Hard-label Distillation в этом контексте:

  1. Генерация "жестких" меток учителем (Большой GPT):

    • Мы берем большой набор текстовых данных (например, обучающую выборку, на которой изначально обучался учитель, или просто большой корпус текстов).

    • Для каждого фрагмента текста (или запроса) из этого набора, мы просим большую модель-учителя сгенерировать текст. В контексте GPT, это означает, что мы подаем учителю входной текст (например, начало предложения или запрос) и просим его сгенерировать продолжение.

    • Учитель генерирует последовательность токенов, которые он считает наиболее вероятными для продолжения данного текста. Эти сгенерированные последовательности токенов и являются нашими "жесткими" метками.

    Пример:

    • Входной текст (запрос): "Столица Франции - это"

    • Учитель (Большая GPT) генерирует: "Париж." (токены: "Па", "ри", "ж", ".")

    • "Жесткая" метка: Последовательность токенов: ("Па", "ри", "ж", ".")

    Мы повторяем этот процесс для большого количества различных входных текстов, получая набор пар: (исходный входной текст, "жесткая" метка - последовательность токенов, сгенерированная учителем).

  2. Обучение студента (Маленький GPT) на "жестких" метках:

    • Теперь у нас есть синтетический датасет, состоящий из пар (исходный входной текст, "жесткая" метка). Мы будем использовать этот датасет для обучения маленькой модели-студента.

    • Мы обучаем студента предсказывать "жесткие" метки, сгенерированные учителем, используя стандартную задачу языкового моделирования. Это означает, что для каждого входного текста мы хотим, чтобы студент генерировал последовательность токенов, максимально похожую на "жесткую" метку, сгенерированную учителем.

    • В процессе обучения мы используем функцию потерь кросс-энтропии. Мы сравниваем распределение вероятностей токенов, предсказанное студентом, с "жесткой" меткой (которая по сути является распределением, где вероятность "правильного" токена равна 1, а всех остальных - 0). Мы стремимся минимизировать эту кросс-энтропию, заставляя студента "подражать" учителю в предсказании токенов.

    В нашем примере, если студент на вход "Столица Франции - это" предсказывает, например, "Лондон", то функция потерь будет высокой, так как "жесткая" метка учителя была "Париж". В процессе обучения студент будет корректировать свои параметры, чтобы в будущем для аналогичных запросов предсказывать "Париж" или что-то очень похожее на предсказание учителя.

Почему маленькая модель может предсказывать те же токены, что и большая?

  • Передача знаний через "жесткие" метки: Хотя Hard-label Distillation и теряет часть информации из распределения вероятностей учителя, она все равно эффективно передает ключевые знания о том, какие токены являются наиболее вероятными в определенных контекстах. Большая модель, будучи хорошо обученной, "знает", какие продолжения текста являются грамматически правильными, семантически уместными и стилистически подходящими. Генерируя "жесткие" метки, она как бы "подсказывает" маленькой модели, какие именно токены нужно предсказывать.

  • Фокус на наиболее важной информации: "Жесткие" метки концентрируются на наиболее вероятных токенах. В языковом моделировании часто бывает так, что для многих контекстов есть один или несколько доминирующих "правильных" продолжений. Hard-label Distillation помогает маленькой модели быстро освоить эти наиболее важные закономерности, игнорируя менее значимые детали, которые могут быть избыточными для достижения хорошего качества генерации.

  • Упрощение задачи обучения: Обучение на "жестких" метках превращает дистилляцию в стандартную задачу обучения с учителем. Это упрощает процесс обучения и позволяет использовать хорошо известные методы и оптимизаторы. Маленькой модели не нужно пытаться воспроизвести все тонкости распределения вероятностей учителя, ей достаточно научиться предсказывать наиболее вероятные токены, что является более простой задачей.

Важно отметить ограничения Hard-label Distillation:

  • Потеря "мягкой" информации: Как и указано в тексте, Hard-label Distillation теряет информацию о вероятностях других классов и "мягких" отношениях между классами. В контексте языковых моделей это означает, что студент может не улавливать все нюансы стиля, семантики и разнообразия, которые присутствуют в распределении вероятностей учителя. Например, учитель может знать, что "Париж" является самым вероятным ответом на "Столица Франции - это", но также понимать, что "Рим" или "Берлин" являются менее вероятными, но все же допустимыми ответами в определенных контекстах. Hard-label Distillation фокусируется только на "Париже", игнорируя эту "мягкую" информацию.

  • Потенциальное ухудшение разнообразия: Из-за фокусировки на "жестких" метках, студент может стать менее разнообразным в своих генерациях, чем учитель. Он может слишком точно копировать наиболее вероятные ответы учителя, упуская возможность генерировать альтернативные, но все еще качественные варианты.

Математическая формализация:

1. Генерация "жестких" меток учителем: Для каждого примераx^{(n)}из обучающей выборки, учительp(y|x)предсказывает распределение вероятностей классов. "Жесткая" меткаy^{(n)}выбирается как класс с максимальной вероятностью, предсказанной учителем. В контексте языков моделей, гдеyпредставляет собой последовательность токенов, учитель генерирует последовательность "жестких" метокy^{(1)}, \ldots y^{(N)}дляNпримеров. Здесьy^{(n)} = (y_1^{(n)}, \ldots, y_{T_n}^{(n)})представляет собой последовательность токенов длинойT_n.

y^{(1)}, \ldots y^{(N)} \sim p(y|x)

В более простом варианте, для классификации, y^{(n)} = \arg\max_{y} p(y|x^{(n)}). В случае последовательностей, учитель может генерировать целые последовательности наиболее вероятных токенов.

2. Обучение студента на "жестких" метках: Студентq_{\theta}(y|x)обучается максимизировать логарифмическую вероятность "жестких" меток, сгенерированных учителем. Это стандартная задача обучения с учителем, где целевыми метками являютсяy^{(1)}, \ldots y^{(N)}. Функция потерь, которую мы минимизируем (или эквивалентно, максимизируем отрицательную потерю), представляет собой ожидание логарифмической вероятности "жестких" меток под распределениемp(y|x)учителя.

\mathbb{E}_{p(y|x)} [\log q_{\theta}(y|x)] \rightarrow \max_{\theta}

В практической реализации, это ожидание аппроксимируется эмпирическим средним по обучающей выборке. Для последовательностей текста, функция потерь выглядит следующим образом:

\frac{1}{N} \sum_{n=1}^{N} \sum_{t=1}^{T_n} \log q_{\theta}(y_t^{(n)}|y_{<t}^{(n)})

Здесь:

* N — количество примеров в обучающей выборке.

* T_n — длина последовательности для $n$-го примера.

* y_t^{(n)} —t-й токен в последовательности "жестких" меток дляn-го примера, сгенерированных учителем.

* y_{<t}^{(n)} = (y_1^{(n)}, \ldots, y_{t-1}^{(n)}) — префикс последовательности доt-го токена.

* q_{\theta}(y_t^{(n)}|y_{<t}^{(n)}) — вероятность предсказания студентомt-го токенаy_t^{(n)} при условии предыдущих токеновy_{<t}^{(n)}, параметризованная\theta.

Эта функция потерь представляет собой кросс-энтропию между распределением "жестких" меток, сгенерированных учителем, и предсказаниями студента. Мы стремимся максимизировать эту величину, что эквивалентно минимизации отрицательной логарифмической правдоподобности или кросс-энтропии.

Преимущества и недостатки Hard-label Distillation:

  • Преимущества: Простота реализации и понимания. Можно использовать стандартные методы обучения с учителем.

  • Недостатки: Потеря информации, содержащейся в распределении вероятностей учителя. "Жесткие" метки содержат только информацию о наиболее вероятном классе, игнорируя вероятности других классов и "мягкие" отношения между классами, которые учитель "знает". Это может ограничить эффективность передачи знаний.

Реализация Hard-label Distillation на основе Open R1

Ниже представлена реализация Hard-label Distillation с использованием подхода, применяемого в проекте Open R1. Процесс разделен на два этапа: генерация данных учителем и обучение ученика.

@misc{openr1,
    title = {Open R1: A fully open reproduction of DeepSeek-R1},
    url = {https://github.com/huggingface/open-r1},
    author = {Hugging Face},
    month = {January},
    year = {2025}
}

Этап 1: Генерация "жестких" меток большой моделью (учителем)

import argparse
from datasets import load_dataset
from typing import Optional, Dict, Any

from distilabel.pipeline import Pipeline
from distilabel.models import vLLM
from distilabel.steps.tasks import TextGeneration

def build_hard_label_pipeline(
    teacher_model: str,
    base_url: str = "http://localhost:8000/v1",
    prompt_column: Optional[str] = None,
    prompt_template: str = "{{ instruction }}",
    temperature: float = 0.0,
    max_new_tokens: int = 4096,
    input_batch_size: int = 32,
) -> Pipeline:
    """
    Description:
    ---------------
        Создает конвейер для генерации "жестких" меток с использованием модели-учителя.

    Args:
    ---------------
        teacher_model: Идентификатор модели-учителя
        base_url: URL сервера vLLM
        prompt_column: Имя колонки в датасете, содержащей входные тексты
        prompt_template: Шаблон для форматирования промптов
        temperature: Температура для генерации (0.0 для "жестких" меток)
        max_new_tokens: Максимальное количество генерируемых токенов
        input_batch_size: Размер батча для входных данных

    Returns:
    ---------------
        Настроенный конвейер Distilabel

    Raises:
    ---------------
        Exception: В случае ошибки настройки конвейера

    Examples:
    ---------------
        >>> pipeline = build_hard_label_pipeline("deepseek-ai/DeepSeek-R1")
        >>> pipeline.run(dataset)
    """
    # Настраиваем параметры генерации с temperature=0 для получения детерминированных ответов
    generation_kwargs: Dict[str, Any] = {
        "max_new_tokens": max_new_tokens,
        "temperature": temperature,
        "top_p": 1.0,
        "do_sample": False,          # Отключаем семплирование для получения "жестких" меток
    }

    with Pipeline(
        name="hard-label-distillation",
        description="Конвейер для генерации 'жестких' меток с использованием модели-учителя",
    ) as pipeline:
        # Настраиваем модель-учителя через vLLM
        teacher = vLLM(
            model=teacher_model,
            tokenizer=teacher_model,
            extra_kwargs={
                "tensor_parallel_size": 1,               # Можно увеличить для больших моделей
                "max_model_len": max_new_tokens + 2048,  # Добавляем запас для контекста
            },
            generation_kwargs=generation_kwargs,
        )

        # Настраиваем шаг генерации текста
        text_generation = TextGeneration(
            llm=teacher,
            template=prompt_template,
            num_generations=1,           # Для "жестких" меток нам нужна только одна генерация
            input_mappings={"instruction": prompt_column} if prompt_column is not None else {},
            input_batch_size=input_batch_size,
        )

    return pipeline

def generate_hard_labels(
    dataset_name: str,
    dataset_split: str = "train",
    teacher_model: str = "deepseek-ai/DeepSeek-R1",
    output_dataset: str = "my-username/hard-label-distill-dataset",
    prompt_column: str = "problem",
    prompt_template: str = "You will be given a problem. Please reason step by step, and put your final answer within \\boxed{}: {{ instruction }}",
    max_examples: Optional[int] = None,
    private: bool = False,
) -> Any:
    """
    Description:
    ---------------
        Генерирует "жесткие" метки с использованием модели-учителя и сохраняет результаты как набор данных на HuggingFace Hub.

    Args:
    ---------------
        dataset_name: Имя исходного датасета
        dataset_split: Имя сплита датасета
        teacher_model: Модель-учитель для генерации "жестких" меток
        output_dataset: Имя выходного датасета на HuggingFace Hub
        prompt_column: Имя колонки, содержащей входные данные
        prompt_template: Шаблон для форматирования промптов
        max_examples: Максимальное количество примеров для обработки
        private: Приватный ли выходной датасет

    Returns:
    ---------------
        Датасет с "жесткими" метками

    Raises:
    ---------------
        Exception: В случае ошибки генерации меток

    Examples:
    ---------------
        >>> hard_label_dataset = generate_hard_labels("my-dataset", "train")
        >>> hard_label_dataset.push_to_hub("my-username/hard-label-dataset")
    """
    # Загружаем исходный датасет
    print(f"Загрузка датасета '{dataset_name}' (сплит: {dataset_split})...")
    dataset = load_dataset(dataset_name, split=dataset_split)

    # Ограничиваем количество примеров, если указано
    if max_examples is not None and max_examples < len(dataset):
        dataset = dataset.select(range(max_examples))

    print(f"Создание конвейера для генерации 'жестких' меток с использованием {teacher_model}...")
    pipeline = build_hard_label_pipeline(
        teacher_model=teacher_model,
        prompt_column=prompt_column,
        prompt_template=prompt_template,
    )

    print(f"Запуск конвейера для генерации 'жестких' меток на {len(dataset)} примерах...")
    # Генерируем "жесткие" метки
    hard_label_dataset = pipeline.run(dataset=dataset)

    # Сохраняем результаты на HuggingFace Hub
    if output_dataset:
        print(f"Сохранение результатов в '{output_dataset}'...")
        hard_label_dataset.push_to_hub(output_dataset, private=private)
        print(f"Датасет с 'жесткими' метками успешно сохранен в '{output_dataset}'.")

    return hard_label_dataset

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Генерация 'жестких' меток с использованием модели-учителя")
    parser.add_argument("--dataset", type=str, required=True, help="Имя исходного датасета")
    parser.add_argument("--split", type=str, default="train", help="Сплит датасета")
    parser.add_argument("--teacher-model", type=str, default="deepseek-ai/DeepSeek-R1", help="Модель-учитель")
    parser.add_argument("--output-dataset", type=str, required=True, help="Имя выходного датасета")
    parser.add_argument("--prompt-column", type=str, default="problem", help="Колонка с входными данными")
    parser.add_argument("--prompt-template", type=str,
                       default="You will be given a problem. Please reason step by step, and put your final answer within \\boxed{}: {{ instruction }}",
                       help="Шаблон для форматирования промптов")
    parser.add_argument("--max-examples", type=int, default=None, help="Максимальное количество примеров")
    parser.add_argument("--private", action="store_true", help="Сделать выходной датасет приватным")

    args = parser.parse_args()

    generate_hard_labels(
        dataset_name=args.dataset,
        dataset_split=args.split,
        teacher_model=args.teacher_model,
        output_dataset=args.output_dataset,
        prompt_column=args.prompt_column,
        prompt_template=args.prompt_template,
        max_examples=args.max_examples,
        private=args.private,
    )

Этап 2: Обучение модели-ученика на "жестких" метках

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional, Dict, Any

import datasets
import torch
import transformers
from datasets import load_dataset
from transformers import AutoTokenizer, set_seed
from transformers.trainer_utils import get_last_checkpoint

from trl import SFTTrainer, ModelConfig, TrlParser, get_peft_config
from open_r1.configs import SFTConfig
from open_r1.utils.wandb_logging import init_wandb_training

logger = logging.getLogger(__name__)

@dataclass
class HardLabelDistillConfig(SFTConfig):
    """Конфигурация для обучения ученика с использованием Hard-label Distillation."""

    dataset_name: str = field(
        default=None, metadata={"help": "Датасет с 'жесткими' метками, сгенерированными учителем"}
    )
    input_column: str = field(
        default="problem", metadata={"help": "Колонка с входными данными"}
    )
    target_column: str = field(
        default="generation_0", metadata={"help": "Колонка с выходными данными (жесткими метками) учителя"}
    )
    max_seq_length: int = field(
        default=2048, metadata={"help": "Максимальная длина последовательности"}
    )

def train_student_model(config: HardLabelDistillConfig, model_args: ModelConfig) -> None:
    """
    Description:
    ---------------
    Обучает модель-ученика на 'жестких' метках, сгенерированных учителем.

    Args:
    ---------------
        config: Конфигурация обучения
        model_args: Конфигурация модели

    Returns:
    ---------------
        None

    Raises:
    ---------------
        Exception: В случае ошибки обучения модели

    Examples:
    ---------------
        >>> train_student_model(config, model_args)
    """
    # Настраиваем логирование
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = config.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)

    # Устанавливаем сид для воспроизводимости
    set_seed(config.seed)

    # Проверяем наличие последнего чекпоинта
    last_checkpoint: Optional[str] = None
    if os.path.isdir(config.output_dir):
        last_checkpoint = get_last_checkpoint(config.output_dir)
        if last_checkpoint is not None:
            logger.info(f"Найден чекпоинт, продолжаем обучение с {last_checkpoint}")

    # Инициализируем Weights & Biases, если нужно
    if "wandb" in config.report_to:
        init_wandb_training(config)

    # Загружаем датасет с 'жесткими' метками
    logger.info(f"Загрузка датасета с 'жесткими' метками: {config.dataset_name}")
    dataset = load_dataset(config.dataset_name)

    # Подготавливаем входные данные и метки для обучения
    def prepare_dataset(examples: Dict[str, Any]) -> Dict[str, Any]:
        """Форматирует данные для обучения с учителем."""
        return {
            "input_ids": examples[config.input_column],
            "labels": examples[config.target_column],
        }

    # Трансформируем датасет
    dataset = dataset.map(prepare_dataset, batched=True)

    # Загружаем токенизатор
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        revision=model_args.model_revision,
        trust_remote_code=model_args.trust_remote_code,
    )

    # Настраиваем chat_template, если указан
    if config.chat_template is not None:
        tokenizer.chat_template = config.chat_template

    # Настраиваем параметры модели
    torch_dtype = (
        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
    )
    model_kwargs: Dict[str, Any] = dict(
        revision=model_args.model_revision,
        trust_remote_code=model_args.trust_remote_code,
        torch_dtype=torch_dtype,
        use_cache=False if config.gradient_checkpointing else True,
    )
    config.model_init_kwargs = model_kwargs

    # Создаем SFT тренер
    trainer = SFTTrainer(
        model=model_args.model_name_or_path,
        args=config,
        train_dataset=dataset["train"],
        eval_dataset=dataset["validation"] if "validation" in dataset and config.eval_strategy != "no" else None,
        processing_class=tokenizer,
        peft_config=get_peft_config(model_args),
    )

    # Запускаем обучение
    logger.info("Начало обучения модели-ученика...")
    checkpoint: Optional[str] = None
    if config.resume_from_checkpoint is not None:
        checkpoint = config.resume_from_checkpoint
    elif last_checkpoint is not None:
        checkpoint = last_checkpoint

    train_result = trainer.train(resume_from_checkpoint=checkpoint)
    metrics = train_result.metrics
    trainer.log_metrics("train", metrics)
    trainer.save_metrics("train", metrics)
    trainer.save_state()

    # Сохраняем модель
    logger.info(f"Сохранение модели в {config.output_dir}")
    trainer.save_model(config.output_dir)

    # Создаем карточку модели и загружаем на HuggingFace Hub, если нужно
    kwargs: Dict[str, Any] = {
        "dataset_name": config.dataset_name,
        "tags": ["hard-label-distillation", "open-r1"],
    }

    if trainer.accelerator.is_main_process:
        trainer.create_model_card(**kwargs)
        # Восстанавливаем кэш для быстрого инференса
        trainer.model.config.use_cache = True
        trainer.model.config.save_pretrained(config.output_dir)

    # Оцениваем модель, если нужно
    if config.do_eval and "validation" in dataset:
        logger.info("Оценка модели...")
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Загружаем модель на HuggingFace Hub, если нужно
    if config.push_to_hub:
        logger.info("Загрузка модели на HuggingFace Hub...")
        trainer.push_to_hub(**kwargs)

if __name__ == "__main__":
    # Создаем парсер аргументов
    parser = TrlParser((HardLabelDistillConfig, ModelConfig))
    config, model_args = parser.parse_args_and_config()

    # Запускаем обучение
    train_student_model(config, model_args)

Пример использования

# Этап 1: Генерация "жестких" меток с использованием модели-учителя
python hard_label_distill.py \
  --dataset AI-MO/NuminaMath-TIR \
  --teacher-model deepseek-ai/DeepSeek-R1 \
  --output-dataset username/hard-label-math-dataset \
  --prompt-column problem

# Этап 2: Обучение модели-ученика на сгенерированных "жестких" метках
accelerate launch --config_file=recipes/accelerate_configs/zero3.yaml train_student.py \
  --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
  --dataset_name username/hard-label-math-dataset \
  --input_column problem \
  --target_column generation_0 \
  --learning_rate 1.0e-5 \
  --num_train_epochs 2 \
  --packing \
  --max_seq_length 4096 \
  --per_device_train_batch_size 8 \
  --gradient_accumulation_steps 4 \
  --gradient_checkpointing \
  --bf16 \
  --output_dir models/Qwen2.5-1.5B-Hard-Label-Distill

II. Soft-label Distillation: Дистилляция с использованием "мягких" меток

Концепция:

Soft-label distillation, предложенная Хинтоном и соавторами в их знаменитой статье "Distilling the Knowledge in a Neural Network" (2015), является более совершенным методом дистилляции знаний. В отличие от Hard-label distillation, этот подход использует не только "жесткие" метки, но и полное распределение вероятностей, предсказанное учителем, в качестве "мягких" меток (soft labels).

"Мягкие" метки содержат значительно больше информации, чем "жесткие", поскольку они отражают уверенность учителя в различных классах и отношения между ними. Например, учитель может предсказать для изображения собаки вероятности [0.8 для "собака", 0.15 для "волк", 0.03 для "лиса", 0.02 для других классов]. Эта информация гораздо богаче, чем просто метка "собака".

Ключевым компонентом метода является "temperature scaling" (масштабирование температуры), который делает распределение вероятностей более "мягким" и информативным путем деления логитов модели на параметр температуры T > 1.

Soft-label Distillation для GPT моделей: объяснение на пальцах

Представьте, что у нас есть две модели:

  • Учитель (Teacher): Большая, мощная GPT модель с 175 миллиардами параметров. Она обладает глубоким пониманием языка и мира.

  • Студент (Student): Компактная GPT модель с 1.5 миллиардами параметров. Намного быстрее и экономичнее, но изначально уступает учителю в качестве.

Наша цель - научить студента генерировать текст так же хорошо, как учитель, используя Soft-label Distillation.

Шаги Soft-label Distillation:

  1. Генерация "мягких" меток учителем:

    • Для запроса "Столица Франции - это" большая модель-учитель не просто выдает "Париж", но вычисляет вероятности для всех возможных следующих токенов:

      • "Париж": 0.92

      • "город": 0.03

      • "Рим": 0.01

      • ... (и тысячи других токенов с малыми вероятностями)

    • Проблема: это распределение слишком "острое" - один токен имеет почти всю вероятность. Чтобы извлечь больше полезных знаний, применяем temperature scaling:

    • Делим логиты на температуру T (например, T = 2.0) перед применением softmax:

      • "Париж": 0.70 (уменьшилось с 0.92)

      • "город": 0.08 (увеличилось с 0.03)

      • "Рим": 0.05 (увеличилось с 0.01)

      • ... (другие токены тоже получают больше вероятности)

    • Эти "смягченные" распределения сохраняют намного больше информации о том, что модель-учитель "знает".

  2. Обучение модели-студента:

    • Студент обучается не только предсказывать правильный токен, но и воспроизводить всё распределение вероятностей учителя.

    • Для этого используется КЛ-дивергенция (или кросс-энтропия) между распределениями учителя и студента.

    • Важно: распределение студента также "смягчается" с той же температурой T для сопоставимости.

    • Функция потерь умножается на T² для компенсации уменьшения градиентов.

  3. Комбинированное обучение:

    • Обычно используется комбинация двух функций потерь:

      • α · (Потери от "мягких" меток) + (1-α) · (Стандартные потери от "жестких" меток)

    • Где α - коэффициент, обычно от 0.5 до 0.9

Почему это работает лучше Hard-label Distillation?

  • "Темные знания" (Dark Knowledge): Как назвал Хинтон, относительные вероятности "неправильных" ответов содержат ценную информацию. Например, если модель путает "собаку" с "волком", но не с "самолетом", это важная информация.

  • Передача неопределенности: Студент учится не только правильным ответам, но и тому, в каких случаях стоит сомневаться.

  • Более богатый сигнал: Вместо одного бита информации на каждый пример (правильный/неправильный класс), студент получает информацию о всем распределении вероятностей.

Математическая формализация:

1. "Мягкие" метки учителя с температурой T:

Еслиz_i- логит для класса (токена)iот учителя, то "мягкая" метка с температурой T:

p_i^T = \frac{\exp(z_i/T)}{\sum_j \exp(z_j/T)}

Разберем каждый элемент формулы:

* p_i^T: Это "мягкая" вероятность дляi-го токена, с учетом температурыT. Именно это распределение вероятностей, сгенерированное учителем, мы будем использовать как "мягкую метку".

* z_i: Это логит (logit) дляi-го токена, выданный моделью-учителем. Логиты - это значения, которые модель выдает перед применением функции softmax. Они представляют собой "сырые" оценки того, насколько модель уверена в каждом токене. Чем больше логит, тем больше уверенность модели в этом токене.

* T: Это параметр температуры (temperature). Как мы разбирали уже выше, температура используется для "смягчения" распределения вероятностей.

* \exp(x): Это экспоненциальная функцияe^x.

* \sum_j \exp(z_j/T): Это сумма экспоненциальных значений логитов, деленных на температуру, для всех возможных токеновj. Эта сумма используется для нормализации, чтобы вероятности в итоге суммировались к 1.

Пошаговое объяснение:

1. Деление логитов на температуруz_i/T: Когда мы делим логиты на температуруT > 1, мы уменьшаем абсолютные значения логитов.

2. Экспоненцирование\exp(z_i/T): Экспоненциальная функция преобразует логиты в положительные значения.

3. Нормализация\frac{\exp(z_i/T)}{\sum_j \exp(z_j/T)}: Деление на сумму экспоненциальных значений всех логитов гарантирует, что полученные значенияp_i^T будут представлять собой вероятностное распределение, то есть будут неотрицательными и в сумме дадут 1. Это стандартная операция softmax, но с применением температуры.

Интуиция и эффект температуры:

* При высокой температуре (например, T = 2.0), распределение вероятностей становится более "мягким" или "ровным". Вероятности для менее вероятных токенов увеличиваются, а вероятность наиболее вероятного токена уменьшается. Это позволяет "вытащить" больше информации из распределения, включая "темные знания" о менее вероятных, но все же релевантных вариантах.

* При низкой температуре (приближающейся к T = 1.0, или даже меньше), распределение становится более "острым". Вероятность наиболее вероятного токена приближается к 1, а вероятности остальных токенов стремятся к 0. При T=1 это стандартный softmax. ПриT \rightarrow 0 распределение становится дельта-функцией, выбирая только токен с наибольшим логитом.

Figure_1.jpg
Figure_1.jpg

2. Аналогично для студента:

q_i^T = \frac{\exp(z_i^q/T)}{\sum_j \exp(z_j^q/T)}

гдеz_i^q - логит студента для классаi.

Аналогия с формулой учителя: Эта формула абсолютно идентична формуле для учителя, за исключением того, что здесь используются логиты, выданные моделью-студентом.

* q_i^T: "Мягкая" вероятность дляi-го токена, сгенерированная студентом с температуройT.

* z_i^q: Логит дляi-го токена, выданный моделью-студентом.

* Цель: Мы применяем ту же температуруT к распределению студента, чтобы сделать его сопоставимым с "мягкими" метками учителя. Это необходимо для корректного расчета функции потерь дистилляции.

3. Функция потерь для Soft-label Distillation:

L_{soft} = T^2 \cdot \text{KL}(p^T || q^T) = T^2 \cdot \sum_i p_i^T \log\frac{p_i^T}{q_i^T}

МножительT^2 компенсирует уменьшение градиентов из-за temperature scaling.

Разберем компоненты:

* L_{soft}: Функция потерь Soft-label Distillation. Это значение, которое мы хотим минимизировать в процессе обучения студента.

* T^2: Квадрат температуры. Этот множитель используется для масштабирования функции потерь и компенсации уменьшения градиентов, вызванного температурой.

* \text{KL}(p^T || q^T): KL-дивергенция ( Kullback-Leibler divergence) между распределением учителя $p^T$ и распределением студентаq^T.

* \sum_i p_i^T \log\frac{p_i^T}{q_i^T}: Это развернутая формула KL-дивергенции для дискретных распределений.

Пошаговое объяснение KL-дивергенции:

1. \frac{p_i^T}{q_i^T}: Отношение вероятности учителя к вероятности студента для каждого токенаi. Если студент предсказывает вероятностьq_i^T близкую к вероятности учителяp_i^T, это отношение будет близко к 1.

2. \log\frac{p_i^T}{q_i^T}: Логарифм этого отношения. Если отношение близко к 1, логарифм будет близок к 0. Еслиq_i^Tсильно отличается отp_i^T, логарифм будет иметь большее абсолютное значение (отрицательное, еслиq_i^T > p_i^T, и положительное, еслиq_i^T < p_i^T).

3. p_i^T \log\frac{p_i^T}{q_i^T}: Умножение наp_i^T взвешивает вклад каждого токена в общую дивергенцию. Токены, которые учитель считает более вероятными (высокоеp_i^T), вносят больший вклад в функцию потерь.

4. \sum_i p_i^T \log\frac{p_i^T}{q_i^T}: Суммирование по всем токенамi дает общую KL-дивергенцию. KL-дивергенция измеряет "расстояние" между двумя распределениями вероятностей. В контексте дистилляции, она измеряет, насколько распределение студентаq^T отличается от распределения учителяp^T.

РольT^2:

* Применение температурыT "смягчает" распределения, что может привести к уменьшению величины градиентов при обучении. Умножение наT^2 масштабирует функцию потерь, чтобы компенсировать это уменьшение и сделать градиенты более значимыми, особенно на ранних этапах обучения. Это эмпирическая коррекция, которая помогает стабилизировать и ускорить обучение.

* ЦельL_{soft}: МинимизируяL_{soft}, мы заставляем распределение вероятностей студентаq^T максимально приблизиться к распределению вероятностей учителяp^T. Студент учится не только предсказывать "правильный" токен, но и имитировать всю "манеру мышления" учителя, выраженную в распределении вероятностей.

4. Комбинированная функция потерь:

L = \alpha \cdot L_{soft} + (1-\alpha) \cdot L_{hard}

гдеL_{hard} - стандартная кросс-энтропия с истинными метками,\alpha - коэффициент баланса.

Разберем компоненты:

* L: Общая функция потерь, используемая для обучения студента.

* \alpha: Коэффициент баланса (обычно от 0.5 до 0.9). Он определяет, насколько сильно мы полагаемся на "мягкие" метки учителя по сравнению со стандартными "жесткими" метками.

* L_{soft}: Функция потерь Soft-label Distillation, которую мы разобрали выше.

* L_{hard}: Стандартная функция потерь "жестких" меток, обычно кросс-энтропия между предсказаниями студента и истинными (one-hot) метками.


L_{hard} (Стандартные потери "жестких" меток):

* В обычной задаче обучения языковой модели, мы имеем "жесткие" метки - это истинные следующие токены в обучающих данных. Например, для фразы "Столица Франции - это Париж", "Париж" является "жесткой" меткой.

* L_{hard} вычисляется как кросс-энтропия между распределением вероятностей, предсказанным студентом (обычно сT=1, то есть стандартный softmax), и one-hot вектором, представляющим истинный токен. Эта функция потерь заставляет студента предсказывать именно "правильный" токен.

КомбинированиеL_{soft} иL_{hard}:

* Комбинирование "мягких" и "жестких" потерь позволяет студенту учиться как у учителя (черезL_{soft}), так и из исходных данных (черезL_{hard}).

* Коэффициент\alpha позволяет настроить баланс.

* Высокое\alpha (например, 0.9) означает, что мы больше полагаемся на знания учителя, переданные через "мягкие" метки. Это может быть полезно, когда учитель обладает значительно лучшими знаниями, чем можно извлечь только из "жестких" меток.

* Низкое\alpha (например, 0.5) означает, что мы в равной степени учитываем как знания учителя, так и "жесткие" метки. Это может быть полезно, когда мы хотим, чтобы студент сохранил способность хорошо работать и на исходных данных, а не только имитировал учителя.

Практическая реализация Soft-label Distillation для GPT моделей

Программный код был заимствован из репозитория: https://github.com/arcee-ai/DistillKit

1. Конфигурация дистилляции

Первым шагом необходимо настроить параметры дистилляции, включая температуру и коэффициент баланса между мягкими и жесткими метками:

"""
Здесь temperature: 2.0 соответствует параметру T в формулах, который "смягчает" распределение вероятностей, а alpha: 0.5 - это коэффициент α, который определяет соотношение между потерями от мягких и жестких меток.
"""

config = {
    "project_name": "distil-multilayer",    # Название проекта
    "dataset": {
        "name": "mlabonne/FineTome-100k",   # Название датасета
        "split": "train",                   # Раздел датасета для тренировки
        "num_samples": 1000,                # Количество образцов для тренировки (можно ограничить)
        "seed": 42                          # Значение для инициализации генератора случайных чисел
    },
    "models": {
        "teacher": "arcee-ai/Arcee-Spark",  # Модель учителя
        "student": "Qwen/Qwen2-1.5B"        # Модель студента
    },
    "tokenizer": {
        "max_length": 4096,                 # Максимальная длина токенов
        "chat_template": (
            "{% for message in messages %}"
            "{% if loop.first and messages[0]['role'] != 'system' %}"
            "{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}"
            "{% endif %}"
            "{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}"
            "{% endfor %}"
            "{% if add_generation_prompt %}"
            "{{ '<|im_start|>assistant\n' }}"
            "{% endif %}"
        )                                    # Шаблон для форматирования сообщений в чате
    },
    "training": {
        "output_dir": "./results",           # Директория для сохранения результатов
        "num_train_epochs": 3,               # Количество эпох для тренировки
        "per_device_train_batch_size": 1,    # Размер батча для тренировки на одном устройстве
        "gradient_accumulation_steps": 8,    # Количество шагов для накопления градиентов
        "save_steps": 1000,                  # Шаги для сохранения модели
        "logging_steps": 2,                  # Шаги для логирования
        "save_total_limit": 2,               # Лимит на количество сохраняемых моделей
        "learning_rate": 2e-5,               # Скорость обучения
        "weight_decay": 0.01,                # Коэффициент регуляризации
        "warmup_ratio": 0.2,                 # Доля шагов для разгона скорости обучения
        "lr_scheduler_type": "linear",       # Тип планировщика скорости обучения
        "resume_from_checkpoint": None,      # Путь к чекпоинту для возобновления тренировки (если есть)
        "fp16": False,                       # Использовать ли 16-битное число с плавающей точкой
        "bf16": True,                        # Использовать ли BFloat16
        "max_grad_norm": 1.0,                # Максимальная норма градиента
        "group_by_length": False             # Группировать ли батчи по длине
    },
    "distillation": {
        "temperature": 2.0,                  # Температура для дистилляции
        "alpha": 0.5                         # Коэффициент альфа для дистилляции
    },
    "model_config": {
        "use_flash_attention": True          # Использовать ли Flash Attention
    }
}

2. Подготовка моделей учителя и студента

Для дистилляции необходимо загрузить как модель-учитель (более крупную), так и модель-студент (более компактную):

import torch
from typing import Dict, Any
from transformers import AutoModelForCausalLM

def load_models_with_flash_attention(config: Dict[str, Any]) -> Dict[str, AutoModelForCausalLM]:
    """
    Description:
    ---------------
        Загружает модели с настройкой флеш-внимания для ускорения.

    Args:
    ---------------
        config: Конфигурация моделей и параметров

    Returns:
    ---------------
        Словарь с загруженными моделями

    Raises:
    ---------------
        KeyError: Если в конфигурации отсутствуют необходимые ключи

    Examples:
    ---------------
        >>> config = {
        ...     "model_config": {"use_flash_attention": True},
        ...     "models": {"teacher": "teacher_model_path", "student": "student_model_path"}
        ... }
        >>> load_models_with_flash_attention(config)
        {'teacher_model': <transformers.models.model_name.model.ModelName object>,
         'student_model': <transformers.models.model_name.model.ModelName object>}
    """
    # Настройки для загрузки моделей
    model_kwargs: Dict[str, Any] = {"torch_dtype": torch.bfloat16}

    # Проверка на использование flash attention
    if config["model_config"]["use_flash_attention"]:
        model_kwargs["attn_implementation"] = "flash_attention_2"

    # Загрузка моделей
    teacher_model = AutoModelForCausalLM.from_pretrained(config["models"]["teacher"], **model_kwargs)
    student_model = AutoModelForCausalLM.from_pretrained(config["models"]["student"], **model_kwargs)

    return {"teacher_model": teacher_model, "student_model": student_model}

# Вызов функции
models = load_models_with_flash_attention(config)

# Теперь models содержит загруженные модели
teacher_model = models["teacher_model"]
student_model = models["student_model"]

3. Реализация функции потерь с мягкими метками

Ключевым компонентом является функция потерь Soft-label Distillation. Рассмотрим её реализацию из файла distil_logits.py:

"""
Это прямая реализация формулы KL-дивергенции. Обратите внимание на следующие ключевые моменты:

1. Логиты масштабируются температурой T перед применением функций softmax/log_softmax.
2. Потери умножаются на T² для компенсации уменьшения градиентов, как описано в теории.
3. Финальная функция потерь комбинирует мягкие метки (KL-дивергенция) и жесткие метки (original_loss) с коэффициентом α.
"""

from typing import Any
import torch
import torch.nn.functional as F

def distillation_loss(
    self,
    student_logits: torch.Tensor,
    teacher_logits: torch.Tensor,
    inputs: Any,
    original_loss: torch.Tensor,
    config: Dict[str, Any]
) -> torch.Tensor:
    """
    Description:
    ---------------
        Вычисляет потери дистилляции между логитами студента и учителя.

    Args:
    ---------------
        student_logits: Логиты студента.
        teacher_logits: Логиты учителя.
        inputs: Входные данные.
        original_loss: Исходные потери.
        config: Конфигурация моделей и параметров.

    Returns:
    ---------------
        Общие потери, включающие дистилляционные потери и исходные потери.

    Raises:
    ---------------
        KeyError: Если в конфигурации отсутствуют необходимые ключи.

    Examples:
    ---------------
        >>> config = {
        ...     "distillation": {"temperature": 2.0, "alpha": 0.5},
        ...     "tokenizer": {"max_length": 512}
        ... }
        >>> student_logits = torch.randn(3, 512)
        >>> teacher_logits = torch.randn(3, 512)
        >>> inputs = ...
        >>> original_loss = torch.tensor(0.5)
        >>> distillation_loss(self, student_logits, teacher_logits, inputs, original_loss, config)
        tensor(0.25)
    """
    # Приведение размерностей логитов учителя и студента к одинаковому размеру
    student_logits, teacher_logits = pad_logits(
        student_logits.to(self.model.device),
        teacher_logits.to(self.model.device)
    )

    # Масштабирование логитов с помощью температуры T
    temperature = config["distillation"]["temperature"]
    student_logits_scaled = student_logits / temperature
    teacher_logits_scaled = teacher_logits / temperature

    # Расчёт KL-дивергенции между распределениями учителя и студента
    loss_kd = F.kl_div(
        F.log_softmax(student_logits_scaled, dim=-1),  # log(q_i^T)
        F.softmax(teacher_logits_scaled, dim=-1),      # p_i^T
        reduction='batchmean'
    ) * (temperature ** 2) / config["tokenizer"]["max_length"]

    # Комбинирование потерь от мягких и жестких меток
    alpha = config["distillation"]["alpha"]
    total_loss = alpha * loss_kd + (1 - alpha) * original_loss

    return total_loss

4. Обработка различных размеров словарей

Поскольку модели учителя и студента могут иметь разный размер словаря токенов, необходима дополнительная функция для согласования размерности их логитов:

"""
Эта функция добавляет нулевые логиты к меньшему распределению, чтобы обеспечить одинаковую размерность для сравнения распределений.
"""

from typing import Tuple
import torch

def pad_logits(
    student_logits: torch.Tensor,
    teacher_logits: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Description:
    ---------------
        Приводит размерности логитов студента и учителя к одинаковому размеру.

    Args:
    ---------------
        student_logits: Логиты студента.
        teacher_logits: Логиты учителя.

    Returns:
    ---------------
        Кортеж из логитов студента и учителя с одинаковыми размерностями.

    Raises:
    ---------------
        ValueError: Если размерности логитов не совпадают и не могут быть приведены к одинаковому размеру.

    Examples:
    ---------------
        >>> student_logits = torch.randn(3, 512)
        >>> teacher_logits = torch.randn(3, 510)
        >>> pad_logits(student_logits, teacher_logits)
        (tensor([...]), tensor([...]))
    """
    # Определение размеров логитов
    student_size, teacher_size = student_logits.size(-1), teacher_logits.size(-1)

    # Если размеры не совпадают, добавляем паддинг
    if student_size != teacher_size:
        pad_size = abs(student_size - teacher_size)
        pad_tensor = torch.zeros(
            (*teacher_logits.shape[:-1], pad_size),
            dtype=teacher_logits.dtype,
            device=teacher_logits.device
        )

        # Возвращаем логиты с добавленным паддингом
        if student_size < teacher_size:
            return torch.cat([student_logits, pad_tensor], dim=-1), teacher_logits
        else:
            return student_logits, torch.cat([teacher_logits, pad_tensor], dim=-1)

    # Возвращаем логиты без изменений, если размеры совпадают
    return student_logits, teacher_logits

5. Кастомный тренер для дистилляции

Для интеграции процесса дистилляции в процесс обучения создаётся специальный класс тренера, который переопределяет функцию вычисления потерь:

"""
Этот класс:
1. Получает выходы (логиты) как от студента, так и от учителя
2. Замораживает веса учителя с помощью `torch.no_grad()`
3. Вычисляет комбинированную функцию потерь с использованием потерь от мягких и жестких меток
"""

from typing import Dict, Any, Union, Tuple
import torch
import torch.nn.functional as F
from transformers import SFTTrainer

class LogitsTrainer(SFTTrainer):
    """
    Description:
    ---------------
        Класс для обучения модели с использованием дистилляции логитов.
    """

    def compute_loss(
        self,
        model: torch.nn.Module,
        inputs: Dict[str, Any],
        return_outputs: bool = False
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, Any]]:
        """
        Description:
        ---------------
            Вычисляет комбинированную функцию потерь для модели студента и учителя.

        Args:
        ---------------
            model: Модель студента.
            inputs: Входные данные.
            return_outputs: Флаг для возврата выходов модели.

        Returns:
        ---------------
            Комбинированная функция потерь и, если указано, выходы модели.

        Raises:
        ---------------
            ValueError: Если входные данные не соответствуют ожидаемым.

        Examples:
        ---------------
            >>> model = ...
            >>> inputs = ...
            >>> trainer = LogitsTrainer()
            >>> trainer.compute_loss(model, inputs, return_outputs=True)
            (tensor(0.5), ...)
        """
        # Перемещение входных данных на устройство модели
        inputs = {k: v.to(model.device) if hasattr(v, 'to') else v for k, v in inputs.items()}

        # Перемещение модели учителя на устройство модели
        self.teacher_model = self.teacher_model.to(model.device)

        # Получение модулей моделей, если они существуют
        student_model = model.module if hasattr(model, 'module') else model
        teacher_model = self.teacher_model.module if hasattr(self.teacher_model, 'module') else self.teacher_model

        # Получение выходов моделей
        student_outputs = student_model(**inputs)
        with torch.no_grad():  # Учитель не обучается
            teacher_outputs = teacher_model(**inputs)

        # Вычисление комбинированной функции потерь
        custom_loss = self.distillation_loss(
            student_outputs.logits,
            teacher_outputs.logits,
            inputs,
            student_outputs.loss
        )

        # Возврат потерь и выходов модели, если указано
        if return_outputs:
            return custom_loss, student_outputs
        return custom_loss

    def pad_logits(
        self,
        student_logits: torch.Tensor,
        teacher_logits: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Description:
        ---------------
            Приводит размерности логитов студента и учителя к одинаковому размеру.

        Args:
        ---------------
            student_logits: Логиты студента.
            teacher_logits: Логиты учителя.

        Returns:
        ---------------
            Кортеж из логитов студента и учителя с одинаковыми размерностями.

        Raises:
        ---------------
            ValueError: Если размерности логитов не совпадают и не могут быть приведены к одинаковому размеру.

        Examples:
        ---------------
            >>> student_logits = torch.randn(3, 512)
            >>> teacher_logits = torch.randn(3, 510)
            >>> trainer = LogitsTrainer()
            >>> trainer.pad_logits(student_logits, teacher_logits)
            (tensor([...]), tensor([...]))
        """
        # Определение размеров логитов
        student_size, teacher_size = student_logits.size(-1), teacher_logits.size(-1)

        # Если размеры не совпадают, добавляем паддинг
        if student_size != teacher_size:
            pad_size = abs(student_size - teacher_size)
            pad_tensor = torch.zeros(
                (*teacher_logits.shape[:-1], pad_size),
                dtype=teacher_logits.dtype,
                device=teacher_logits.device
            )

            # Возвращаем логиты с добавленным паддингом
            if student_size < teacher_size:
                return torch.cat([student_logits, pad_tensor], dim=-1), teacher_logits
            else:
                return student_logits, torch.cat([teacher_logits, pad_tensor], dim=-1)

        # Возвращаем логиты без изменений, если размеры совпадают
        return student_logits, teacher_logits

    def distillation_loss(
        self,
        student_logits: torch.Tensor,
        teacher_logits: torch.Tensor,
        inputs: Any,
        original_loss: torch.Tensor
    ) -> torch.Tensor:
        """
        Description:
        ---------------
            Вычисляет потери дистилляции между логитами студента и учителя.

        Args:
        ---------------
            student_logits: Логиты студента.
            teacher_logits: Логиты учителя.
            inputs: Входные данные.
            original_loss: Исходные потери.

        Returns:
        ---------------
            Общие потери, включающие дистилляционные потери и исходные потери.

        Raises:
        ---------------
            KeyError: Если в конфигурации отсутствуют необходимые ключи.

        Examples:
        ---------------
            >>> config = {
            ...     "distillation": {"temperature": 2.0, "alpha": 0.5},
            ...     "tokenizer": {"max_length": 512}
            ... }
            >>> student_logits = torch.randn(3, 512)
            >>> teacher_logits = torch.randn(3, 512)
            >>> inputs = ...
            >>> original_loss = torch.tensor(0.5)
            >>> trainer = LogitsTrainer()
            >>> trainer.distillation_loss(student_logits, teacher_logits, inputs, original_loss)
            tensor(0.25)
        """
        # Приведение размерностей логитов учителя и студента к одинаковому размеру
        student_logits, teacher_logits = self.pad_logits(
            student_logits.to(self.model.device),
            teacher_logits.to(self.model.device)
        )

        # Масштабирование логитов с помощью температуры T
        temperature = config["distillation"]["temperature"]
        student_logits_scaled = student_logits / temperature
        teacher_logits_scaled = teacher_logits / temperature

        # Расчёт KL-дивергенции между распределениями учителя и студента
        loss_kd = F.kl_div(
            F.log_softmax(student_logits_scaled, dim=-1),  # log(q_i^T)
            F.softmax(teacher_logits_scaled, dim=-1),      # p_i^T
            reduction='batchmean'
        ) * (temperature ** 2) / config["tokenizer"]["max_length"]

        # Комбинирование потерь от мягких и жестких меток
        alpha = config["distillation"]["alpha"]
        total_loss = alpha * loss_kd + (1 - alpha) * original_loss

        return total_loss

6. Подготовка тренера и запуск обучения

После определения всех компонентов можно инициализировать тренер и запустить процесс дистилляции:

"""
Обратите внимание, что модель-учитель добавляется к тренеру как атрибут, чтобы она была доступна внутри функции `compute_loss`.
"""

# Импорт необходимых библиотек
from transformers import TrainingArguments
from accelerate import Accelerator

# Инициализация accelerator
accelerator = Accelerator()

# Аргументы обучения
training_arguments = TrainingArguments(**config["training"])

# Проверка наличия предобработанного датасета
if 'tokenized_dataset' not in locals():
    # Если датасет не предобработан, выполняем необходимую предобработку
    # Код предобработки датасета должен быть здесь...
    print("Необходимо сначала выполнить предобработку датасета!")

# Создание кастомного SFT тренера
trainer = LogitsTrainer(
    model=student_model,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["test"],
    tokenizer=student_tokenizer,
    args=training_arguments,
    max_seq_length=config["tokenizer"]["max_length"],
    dataset_text_field="text",
)

# Добавление модели-учителя к тренеру
trainer.teacher_model = teacher_model

# Подготовка к распределенному обучению
trainer = accelerator.prepare(trainer)

# Запуск обучения
trainer.train(resume_from_checkpoint=config["training"]["resume_from_checkpoint"])

# Сохранение финальной модели
trainer.save_model(config["training"]["output_dir"])

print(f"Обучение завершено. Модель сохранена в {config['training']['output_dir']}")

Преимущества Soft-label Distillation:

  • Более полная передача знаний: Студент получает доступ к "темным знаниям" учителя — информации о сложных случаях, тонких различиях между классами и степени неопределенности.

  • Лучшие результаты: Студенты, обученные этим методом, обычно демонстрируют производительность ближе к учителю по сравнению с Hard-label Distillation.

  • Улучшенная генерализация: Модели лучше работают на новых данных, так как учатся не только "что" предсказывать, но и "с какой уверенностью".

  • Контроль через температуру: Параметр T позволяет настраивать степень "мягкости" дистилляции. Более высокие значения T делают распределение более равномерным, помогая передать больше информации о маловероятных классах.

  • Совместимость с другими методами: Легко комбинируется с другими техниками улучшения моделей.

Недостатки Soft-label Distillation:

  • Вычислительные затраты: Для языковых моделей с большими словарями (50,000+ токенов) хранение и передача полных распределений вероятностей требует значительных ресурсов.

  • Сложность реализации: Требует доступа к логитам/вероятностям учителя, а не только к финальным предсказаниям.

  • Настройка гиперпараметров: Необходимо тщательно подбирать температуру T и коэффициент α для оптимальных результатов.

  • Зависимость от качества учителя: Если учитель имеет систематические ошибки, они могут передаться студенту.

Сравнение Hard-label и Soft-label Distillation:

Аспект

Hard-label Distillation

Soft-label Distillation

Передаваемая информация

Только итоговые классы/токены

Полные распределения вероятностей

Температура

Не используется

Используется для "смягчения" распределений

Сложность реализации

Простая

Средняя

Вычислительные требования

Низкие

Средние-высокие

Объем хранимых данных

Малый

Большой (особенно для языковых моделей)

Качество получаемой модели

Хорошее

Лучшее

Способность передавать неопределенность

Низкая

Высокая

Эффективность для языковых моделей

Средняя

Высокая

В заключение, Soft-label Distillation предлагает более мощный метод передачи знаний от учителя к ученику, особенно для сложных задач, где важны тонкие различия между классами и понимание неопределенности. Ключевое отличие от Hard-label Distillation заключается в использовании полных распределений вероятностей и temperature scaling, что позволяет извлечь "темные знания" и научить студента не только выдавать правильные ответы, но и воспроизводить тонкие нюансы рассуждений учителя.

Part 2: Законы масштабирования дистилляции

После того, как DeepSeek представил в open source свой метод дистилляции знаний для R1, исследователи из Apple и Оксфордского университета быстро предложили закон масштабирования дистилляции и уже 28 февраля завершили все эксперименты и загрузили 67-страничную статью на arXiv.

Рассмотрим мотивацию исследования, которая сводится к следующим пунктам:

  1. Текущее состояние исследований законов масштабирования моделей: В последние годы исследования выявили взаимосвязь между производительностью языковых моделей, их размером и объемом данных для обучения. Однако систематических исследований законов масштабирования в контексте дистилляции пока не проводилось.

  2. Проблема стоимости вывода модели: С увеличением размера языковых моделей значительно возрастает стоимость вывода. Исследование того, как снизить стоимость вывода без потери производительности, становится важной задачей.

  3. Эффективность и производительность дистилляции: Теоретически, дистилляция может снизить стоимость вывода, однако в академических кругах нет единого мнения относительно методов дистилляции, особенно в том, как рационально распределить вычислительные ресурсы для создания наиболее мощных моделей, что остается большой неопределенностью.

Экстраполяция закона масштабирования дистилляции
Экстраполяция закона масштабирования дистилляции

Экстраполяции закона масштабирования дистилляции. Закон масштабирования дистилляции (Уравнение 8) аппроксимирован на слабых учениках L_S > 2.3 для ряда учителей с потерямиL_T. Сплошные линии представляют прогнозируемое поведение модели для невидимых учителей при заданной конфигурации ученика (интерполяция), а пунктирные линии представляют прогнозируемое поведение модели за пределами видимых учителей и для области сильных учеников ( L_S \leq 2.3 ).

Закон масштабирования дистилляции

Традиционный закон масштабирования (Scaling Laws) для больших моделей демонстрирует, что производительность языковой модели (LM) может улучшаться с увеличением вычислительных ресурсов, если модель следует оптимальной вычислительной парадигме обучения. Однако постоянный рост затрат на инференс делает этот подход все менее практичным, что заставляет исследователей искать альтернативные методы, включая переобучение и дистилляцию, для создания небольших, но мощных моделей.

Исследователи провели обширные эксперименты, используя модели-студенты и модели-учителя с параметрами от 143 миллионов до 12,6 миллиардов и объемом данных до 512 миллиардов токенов. Целью было изучить взаимосвязь между производительностью модели и вычислительными ресурсами в процессе дистилляции, а также найти способы оптимизации распределения этих ресурсов.

В следующей таблице показано значение символов, используемых в этой статье:

Таблица. Выражения, связанные с законами масштабирования, используемые в данной работе. В каждом случаеS всегда относится к ученику, а не к обучению с учителем.

Выражение

Значение

N / N_S / N_T

Количество параметров модели/ученика/учителя, не связанных с эмбеддингом. В тексте, когда мы упоминаем параметры, мы всегда имеем в виду параметры, не связанные с эмбеддингом, если не указано иное. Подробности см. в Приложении H.2.

D / D_T

Количество токенов, на которых предобучена модель/учитель.

D_S

Количество токенов, на которых дистиллирован ученик.

M \equiv D / N

Соотношение токенов на параметр, или MM-соотношение. В работе Hoffmann et al. (2022), M принимает оптимальное значение M^∗≈20, что является эмпирическим правилом Chinchilla.

L \approx L(N, D)

Кросс-энтропия модели, которая представляет собой валидационную кросс-энтропию модели на данных, оцениваемую по закону масштабирования с учителем для модели с N параметрами, обученной на D токенах. (Уравнение 1).

L_T \approx L(N_T, D_T)

Кросс-энтропия учителя, которая представляет собой валидационную кросс-энтропию учителя на данных, оцениваемую по закону масштабирования с учителем для учителя с N_T​ параметрами, обученного на D_T​ токенах.

L_S \approx L_S(N_S, D_S, L_T)

Кросс-энтропия ученика, которая представляет собой валидационную кросс-энтропию ученика на данных, оцениваемую по нашему закону масштабирования дистилляции для ученика с N_S​ параметрами, дистиллированного на D_S​ токенах с использованием учителя с потерей предобучения L_T​ (Уравнение 8).

\tilde{L}_S \approx L(N_S, D_S)

Кросс-энтропия ученика с учителем, которая представляет собой валидационную кросс-энтропию ученика на данных, если бы ученик был обучен с учителем, оцениваемую по закону масштабирования с учителем для ученика с N_S​ параметрами, обученного наD_S​ токенах.

Пояснение: Кросс-энтропия — это метрика, измеряющая расхождение между предсказанным распределением вероятностей модели и истинным распределением. Чем ниже кросс-энтропия, тем лучше модель предсказывает правильные токены. Это основной показатель качества языковой модели.

Пояснение к правилу Чинчиллы: Исследование Hoffmann et al. (2022) установило эмпирическое правило оптимального соотношения между количеством параметров модели и количеством токенов для обучения — примерно 20 токенов на каждый параметр. Это правило позволяет эффективно распределять вычислительные ресурсы при обучении крупных языковых моделей.

Формализация закона масштабирования дистилляции

Центральным вкладом исследования является формулировка закона масштабирования дистилляции:

L_S(N_S, D_S, L_T) = L_T + \frac{1}{L_{c_0}^T} \left( 1 + \left( \frac{L_T}{\tilde{L}_S^{d_1}} \right)^{1/f_1} \right)^{-c_1f_1} \left( \frac{A}{N_S^{\alpha'}} + \frac{B}{D_S^{\beta'}} \right)^{\gamma'}

Объяснение переменных:

L_S(N_S, D_S, L_T) — кросс-энтропия студента (мера ошибки предсказания; чем ниже, тем лучше модель).

L_T — кросс-энтропия учителя (мера ошибки предсказания большой модели).

N_S — количество неэмбеддинговых параметров студента (основные обучаемые параметры модели).

D_S — количество токенов, использованных для обучения студента при дистилляции.

\tilde{L}_S = L(N_S, D_S) — потенциальная кросс-энтропия студента при обычном обучении без дистилляции, определяемая классическим законом масштабирования:

L(N, D) = E - \frac{A}{N^\alpha} - \frac{B}{D^\beta}

\{c_0, c_1, d_1, f_1, \alpha', \beta', \gamma'\} — коэффициенты, определяемые эмпирически.

A иB — положительные коэффициенты, зависящие от архитектуры модели и характеристик набора данных.

Физический смысл формулы:

1. Базовая часть:L_T — студент не может быть лучше учителя.

2. Модифицирующая часть: Остальная часть формулы описывает, насколько эффективно студент может приблизиться к учителю в зависимости от своего размера, количества данных и качества учителя.

Ключевые выводы:

1. Студент не может превзойти учителя (всегдаL_S \geq L_T). Кросс-энтропия (L) - это мера ошибки модели. Чем ниже значение L, тем лучше модель предсказывает данные.

2. Чем ближе потенциальная производительность студента к производительности учителя, тем эффективнее дистилляция.

3. При фиксированном учителе закон масштабирования дистилляции не превосходит обычный закон масштабирования.

Практическое применение:

Этот закон позволяет оптимально распределить вычислительные ресурсы между учителем и студентом и прогнозировать эффективность дистилляции.

То есть: Этот закон описывает, как качество маленькой модели зависит от трех факторов: размера самой модели, количества данных для обучения и качества большой модели-учителя. Ключевой вывод: студент никогда не может быть лучше учителя, но насколько близко он подойдет к учителю, зависит от его собственных возможностей и объема тренировки.

Коэффициенты смешивания в процессе дистилляции знаний

Рассмотрев общий закон масштабирования дистилляции, важно также понять практические аспекты реализации этого процесса, в частности, как управлять балансом между имитацией учителя и самостоятельным обучением модели-ученика.

Основная идея дистилляции знаний заключается в переносе информации от большой модели-учителя к компактной модели-ученику. В этом процессе прогнозируемое распределение вероятностей модели-учителя используется в качестве целевого распределения для модели-ученика. Обучение происходит путем минимизации расхождения Кульбака-Лейблера (KL-дивергенции) между распределениями ученика и учителя:

{L}_{\text{KD}} \left( z_T^{(i)}, z_S^{(i)} \right) = -\tau^2 \sum_{a=1}^V \sigma_a \left( \frac{z_T^{(i)}}{\tau} \right) \log \sigma_a \left( \frac{z_S^{(i)}}{\tau} \right)

где:

-z_T^{(i)} иz_S^{(i)} — выходные логиты моделей учителя и ученика соответственно

-\tau — температура дистилляции, контролирующая "сглаженность" распределения вероятностей учителя

-\sigma_a — функция softmax, преобразующая логиты в вероятности

-V — размер словаря

Комбинированная функция потерь для модели-ученика объединяет несколько компонентов:

{L}_S\big(x^{(i)}, \boldsymbol{z}_T^{(i)},\boldsymbol{z}_S^{(i)}\big) = (1-\lambda)\,{L}_{\textrm{NTP}}(x^{(i)},\boldsymbol{z}_S^{(i)}) + \lambda\,{L}_{\textrm{KD}}(\boldsymbol{z}_T^{(i)},\boldsymbol{z}_S^{(i)}) + \lambda_Z\,{L}_Z(\boldsymbol{z}_S^{(i)})

где:

-{L}_{\textrm{NTP}} — потеря при предсказании следующего токена (стандартная кросс-энтропия)

-{L}_{\textrm{KD}}— потеря при дистилляции знаний (KL-дивергенция)

-{L}_Z— регуляризационная Z-потеря, стабилизирующая обучение путем нормализации логитов

-\lambda— коэффициент смешивания, определяющий баланс между обучением на "чистых" данных и имитацией учителя

-\lambda_Z— весовой коэффициент для Z-потери

Экспериментальное определение оптимальных параметров дистилляции

Для определения влияния параметров дистилляции на эффективность закона масштабирования, исследователи провели серию экспериментов. Чтобы исключить влияние данных и сосредоточиться именно на роли модели-учителя, эксперименты проводились в режиме "чистой дистилляции" с λ=1. Результаты показали, что такой выбор λ даёт результаты, статистически сопоставимые с использованием оптимальных значений λ^∗.

Во всех экспериментах использовалась фиксированная температура дистилляции τ=1, которая эмпирически показала наилучшую эффективность для обучения модели-ученика.

Коэффициенты смешивания λ
Коэффициенты смешивания λ

Коэффициенты смешивания\lambda.

(a) Модели-ученики шести размеровN_S \in \{198M, 266M, \ldots, 2.72B\}, обученные с соотношением M = D_S/N_S = 20, дистиллируются от моделей-учителей размеров N_T \in \{546M, 975M, \ldots, 7.75B\}, обученных с соотношениеM = D_T/N_T = 20, с различными значениями коэффициента смешивания \lambda \in [0, 1]. Значения\lambda = 0 и\lambda = 1 соответствуют стандартному обучению и чистой дистилляции соответственно.

(b) Оптимальные коэффициенты смешивания\lambda^* = \arg \min_{\lambda} {L}(\lambda), дающие наименьшую потерю на валидационном наборе для каждой пары учитель-ученик.

Эти эксперименты подтверждают, что параметры дистилляции оказывают существенное влияние на итоговую производительность модели-ученика, и их оптимальный выбор напрямую связан с размерами моделей учителя и ученика, что согласуется с общим законом масштабирования дистилляции.

Вывод

Дистилляция знаний — это метод, позволяющий передать способности большой нейронной модели (учителя) меньшей и вычислительно эффективной модели (ученику). Процесс основан на обучении модели-ученика имитировать распределение вероятностей модели-учителя путём минимизации расхождения Кульбака-Лейблера между их предсказаниями.

Эффективность дистилляции определяется балансом нескольких компонентов в функции потерь:

  • Стандартной кросс-энтропии при предсказании следующего токена

  • KL-дивергенции при имитации учителя

  • Регуляризационной Z-потери для стабилизации обучения

Два ключевых параметра контролируют этот процесс:

  • Коэффициент смешивания λ, регулирующий баланс между самостоятельным обучением и имитацией учителя

  • Температура дистилляции τ, влияющая на "сглаженность" распределения вероятностей

Экспериментальные исследования демонстрируют, что режим "чистой дистилляции" (λ = 1) при температуре τ = 1 часто даёт результаты, сопоставимые с оптимально подобранными параметрами. Однако наиболее важным открытием является то, что идеальные значения этих параметров системно зависят от соотношения размеров конкретной пары моделей учитель-ученик.

Это открытие соответствует общему закону масштабирования дистилляции и имеет прямое практическое применение: для достижения максимальной эффективности при практической реализации дистилляции необходим индивидуальный подбор параметров с учётом размеров используемых моделей, что позволяет существенно улучшить итоговую производительность компактной модели при сохранении её вычислительной эффективности.

Эксперимент с фиксированным учитилем и разными учениками

Размер модели учителя и объем обучающих данных на которых обучался учитель, фиксированы, а размер модели ученика и объем дистилляционных данных варьируются. Цель состоит в том, чтобы изучить, как производительность модели ученика меняется в зависимости от ее размера и объема обработанных дистилляционных данных в условиях фиксированной модели учителя. Таким образом, можно определить оптимальную производительность модели студента при различных масштабах и объемах данных.

Figure_5
Figure_5
Figure_6
Figure_6

Из результатов эксперимента можно заметить, что:

  • При высокой вычислительной мощности, чем больше масштаб параметров модели ученика, тем меньше его функция потерь, и чем больше масштаб модели учителя, тем очевиднее эта тенденция.

  • Когда размер моделей ученика и учителя определен, становится понятно, что чем больше вычислительная мощность, тем лучше будет работать модель ученика.

  • При низкой вычислительной мощности производительность модели сначала улучшится, а затем ослабнет с размером модели. Здесь легко понять, что более крупные модели не полностью обучаются при меньшей вычислительной мощности.

  • В особых случаях модель ученика может превзойти модель учителя и показать способность к обобщению. Я лично предполагаю, что модель учителя может быть недообучена в таких сценариях.

Эксперимент с фиксированным учеником и разными учителями

Размер модели ученика и объем данных дистилляции фиксированы, а размер модели учителя и объем обучающих данных варьируются. Цель состоит в том, чтобы изучить, как эффективность модели учителя влияет на конечную эффективность модели ученика. Таким образом, можно определить оптимальный размер модели учителя и объем обучающих данных для максимизации производительности модели ученика.

Figure_7
Figure_7

Как видно из результатов, чем больше параметры у модели учителя, тем ниже перекрестная энтропия модели ученика. Это показывает, что для достижения наилучшего эффекта дистилляции производительность модели учителя должна соответствовать возможностям модели ученика.

Дистилляция против контролируемого обучения

Чтобы понять, когда дистилляция приносит пользу, на следующем рисунке сравнивается производительность дистилляции и контролируемого обучения при фиксированных вычислительных ресурсах. Результаты показывают, что контролируемое обучение всегда превосходит дистилляцию при наличии достаточного количества вычислений или данных у учащихся. При умеренном бюджете данных дистилляция имеет преимущества, однако при наличии больших объемов данных контролируемое обучение превосходит дистилляцию.

Подводя итог, можно сказать, что при ограниченных вычислительных ресурсах дистилляция обычно более эффективна, чем контролируемое обучение. Это связано с тем, что дистилляция может быстрее усваивать эффективные представления признаков под руководством модели учителя, тем самым достигая более высокой производительности при меньших вычислительных ресурсах.

Figure_8
Figure_8

Выбор модели учителя

  • Сила обучающего сигнала: Модели учителей разных размеров могут обеспечивать разную силу обучающего сигнала, которая обычно измеряется с помощью потери перекрестной энтропии. Более крупная модель учителя может обеспечить более сильный сигнал обучения (более низкая перекрестная энтропия), тем самым помогая модели ученика лучше учиться.

  • Увеличение затрат: использование более крупной модели учителя повлечет за собой более высокие затраты из-за необходимости вычисления логитов модели учителя. Это означает, что более крупная модель учителя не только более затратна в обучении, но и потребляет больше вычислительных ресурсов при использовании для дистилляции.

На рисунке ниже показано изменение потери перекрестной энтропии модели студента при различных бюджетах данных дистилляции. Результаты показывают, что оптимальная потеря учителя (представленная красной линией) уменьшается по степенному закону с увеличением численности учащихся до тех пор, пока потеря ученика не сравняется с оптимальной потерей учителя.

Figure_9
Figure_9

Как видно на другом рисунке ниже, по мере увеличения объема данных дистилляции, перекрестная энтропия оптимальной модели учителя постепенно уменьшается. Таким образом, можно сделать вывод, что: когда вычислительные ресурсы ограничены, выбор меньшей модели учителя может снизить затраты на вывод, при этом обеспечивая эффективные сигналы обучения для модели ученика.

Figure_10
Figure_10

Рассчитайте оптимальную дистилляцию

Целью вычислительно оптимальной дистилляции является определение способа создания модели студента желаемого размера с наименьшей перекрестной энтропией при заданном вычислительном бюджете. В частности, необходимо найти оптимальный объем данных для обучения учащихся, размер модели учителя, данные для обучения, чтобы минимизировать перекрестную энтропию студента и при этом удовлетворить ограничениям вычислительного бюджета.

На рисунке ниже мы видим:

  • Контролируемое обучение всегда соответствует наилучшему варианту настройки дистилляции при достаточном вычислительном бюджете: Контролируемое обучение всегда соответствует наилучшему варианту настройки дистилляции при определенном общем вычислительном бюджете. Это означает, что контролируемое обучение может достичь той же производительности, что и дистилляция, если вычислительный бюджет достаточно велик.

  • Если в вычисления включено обучение учителя, перекрестная энтропия учащихся всегда выше, чем в контролируемой обстановке: это означает, что если вашей единственной целью является создание наилучшей модели с целевым размером и у вас нет доступа к учителю, вам следует выбрать контролируемое обучение вместо обучения учителя и последующей дистилляции. Напротив, если цель состоит в том, чтобы выделить семейство моделей или использовать учителя в качестве обслуживающей модели, то выделение может оказаться более выгодным с вычислительной точки зрения, чем контролируемое обучение.

  • Меньшие модели с большей вероятностью получат выгоду от контролируемого предварительного обучения, в то время как более крупные модели с большей вероятностью получат выгоду от дистилляции: Меньшие модели с большей вероятностью получат выгоду от контролируемого обучения при больших вычислительных бюджетах, в то время как более крупные модели с большей вероятностью получат выгоду от дистилляции при больших вычислительных бюджетах.

Figure_11
Figure_11

На рисунке ниже показаны тенденции изменения оптимального размера учителя и объема обучающих данных по мере изменения вычислительного бюджета. Токены моделей студентов и преподавателей масштабируются по степенному закону, причем токены студентов растут быстрее. Размер лучшей модели учителя сначала увеличивается, пока не станет немного больше ученика, а затем стабилизируется. Это связано с тем, что использование большой модели учителя для вывода обходится дорого, и по мере увеличения количества токенов учеников более эффективным становится переобучение модели учителя.

Figure_12
Figure_12

Ключевые результаты исследования

В результате исследований авторы пришли к следующим выводам:

  1. Предсказуемость производительности через закон масштабирования: Производительность модели-студента размером N_S​, полученной путем дистилляции из модели-учителя размером N_T​ с использованием D_S​ токенов, может быть предсказана с помощью разработанного закона масштабирования дистилляции.

    Практическое значение: Это позволяет заранее оценить, какой результат можно получить от процесса дистилляции, не проводя дорогостоящих экспериментов. Компания может спланировать свои ресурсы и решить, стоит ли вкладываться в дистилляцию, или лучше выбрать другой подход к созданию эффективной модели.

  2. Влияние параметров учителя на студента: Размер модели-учителя NTNT​ и количество токенов для её обучения D_T​ определяют кросс-энтропию модели-учителя L_T=L_T(N_T,D_T), которая, в свою очередь, влияет на кросс-энтропию модели-студента.

    Наглядный пример: Представьте учителя как источник знаний для студента. Если учитель сам недостаточно образован (высокая кросс-энтропия), он не сможет хорошо обучить студента, какими бы способностями студент ни обладал.

  3. Феномен "разрыва в способностях": Исследование выявило интересный эффект - более сильный учитель может привести к худшему студенту, что объясняется "разрывом в способностях" (capacity gap). Влияние кросс-энтропии модели-учителя на потери модели-студента следует степенному закону, который переключается между двумя режимами в зависимости от относительной способности к обучению студента и учителя. Исследование показало, что важен именно разрыв в способности к обучению (гипотезное пространство и оптимизационная способность) между учителем и студентом, а не просто их относительный размер.

    Аналогия для понимания: Представьте, что профессор квантовой физики пытается обучить первоклассника. Несмотря на высокую квалификацию профессора, первоклассник не сможет усвоить сложный материал из-за разрыва в способностях к обучению. Аналогично, если модель-учитель слишком сложна и "мыслит" на уровне, недоступном модели-студенту, эффективность обучения снижается.

  4. U-образная зависимость ошибки студента: Эмпирически подтверждается U-образная зависимость ошибки студента от размера учителя при фиксированном размере студента, что теоретически обосновывается разрывом в емкости между ними.

    Визуальное представление: Если изобразить ошибку студента на графике, где по горизонтальной оси отложен размер учителя, мы увидим U-образную кривую. Это означает, что существует оптимальный размер учителя для данного студента — не слишком маленький (недостаточно знаний) и не слишком большой (слишком сложное представление знаний).

Практические рекомендации

Результаты исследования показывают, что дистилляция становится более эффективной, чем обучение с учителем, при соблюдении следующих условий:

  1. Общее количество вычислений или токенов для студента не превышает пороговое значение, связанное с размером студента, согласно новому закону масштабирования.

    Практический сценарий: Для компании с ограниченным бюджетом на вычисления, которая хочет создать модель размером 1 миллиард параметров, дистилляция может быть оптимальным выбором, если доступно менее 20 миллиардов токенов для обучения (согласно правилу Чинчиллы).

  2. Модель-учитель уже существует, или обучение модели-учителя имеет применение за пределами одной дистилляции.

    Бизнес-кейс: Если компания уже обучила крупную модель для своих основных задач, имеет смысл использовать её для дистилляции меньших, специализированных моделей для развертывания на мобильных устройствах или в средах с ограниченными ресурсами.

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  • 22.01.25 21:43 DoraJaimes23

    Recovery expert. I lost my bitcoin to fake blockchain impostors on Facebook, they contacted me as blockchain official support and i fell stupidly for their mischievous act, this made them gain access into my blockchain wallet whereby 7.0938 btc was stolen from my wallet in total .I was almost in a comma and dumbfounded because this was all my savings i relied on . Then I made a research online and found a recovery expert , with the contact address- { RECOVERYHACKER101 (@) GMAIL . COM }... I wrote directly to the specialist explaining my loss. Hence, he helped me recover a significant part of my investment just after 2 days he helped me launch the recovery program , and the culprits were identified as well , all thanks to his expertise . I hope I have been able to help someone as well . Reach out to the recovery specialist to recover you lost funds from any form of online scam Thanks

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  • 23.01.25 02:37 [email protected]

    After falling victim to a fraudulent Bitcoin mining scam, I found myself in a desperate situation. I had invested $50,000 into a cloud mining website called Miningpool, which turned out to be a complete scam. For months, I tried reaching out to the company, but I was unable to access my funds, and I quickly realized I had been taken for a ride. In my search for help, I came across TrustGeeks Hack Expert, a service that claimed to help people recover lost funds from crypto scams. Though skeptical at first, I decided to give them a try. Here’s my experience with their service.When I initially contacted TrustGeeks Hack Expert Email.. Trustgeekshackexpert{At}fastservice{Dot}com , I was understandably hesitant. Like many others, I had been tricked into believing my Bitcoin investments were legitimate, only to discover they were locked in a non-spendable wallet with no way of accessing them. However, after sharing my story and details about the scam, the team assured me they had handled similar cases and had the expertise to help. They requested basic information about my investment and began their investigation immediately. The recovery process was nothing short of professional. Unlike many other services that promise quick fixes but fail to deliver, TrustGeeks Hack Expert kept me informed at every stage. They regularly updated me on their progress and were completely transparent about the challenges they faced. There were moments when I wondered if the process would work, but the team’s professionalism and reassurance gave me hope. They were honest about the time it would take and did not make any unrealistic promises, which I truly appreciated. After several weeks of work, TrustGeeks Hack Expert successfully recovered not just my $50,000 investment, but also the so-called profits that had been locked away in the scam's non-spendable wallet. This was a huge relief, as I had resigned myself to the idea that I had lost everything. The entire recovery process was discreet and handled with the utmost care, ensuring that the scam company remained unaware of the recovery efforts, which helped prevent further complications. TeleGram iD. Trustgeekshackexpert & What's A p p +1 7 1 9 4 9 2 2 6 9 3

  • 23.01.25 14:20 nellymargaret

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  • 26.01.25 03:54 [email protected]

    Losing access to my crypto wallet account was one of the most stressful experiences ever. After spending countless hours building up my portfolio, I suddenly found myself locked out of my account without access. To make matters worse, the email address I had linked to my wallet was no longer active. When I tried reaching out, I received an error message stating that the domain was no longer in use, leaving me in complete confusion and panic. It was as though everything I had worked so hard for was gone, and I had no idea how to get it back. The hardest part wasn’t just the loss of access it was the feeling of helplessness. Crypto transactions are often irreversible, and since my wallet held significant investments, the thought that my hard-earned money could be lost forever was incredibly disheartening. I spent hours scouring forums and searching for ways to recover my funds, but most of the advice seemed either too vague or too complicated to be of any real help. With no support from the wallet provider and my email account out of reach, I was left feeling like I had no way to fix the situation.That’s when I found out about Trust Geeks Hack Expert . I was hesitant at first, but after reading about their expertise in recovering lost crypto wallets, I decided to give them a try. I reached out to their team, and from the very beginning, they were professional, understanding, and empathetic to my situation. They quickly assured me that there was a way to recover my wallet, and they got to work immediately.Thanks to Trust Geeks Hack Expert , my wallet and funds were recovered, and I couldn’t be more grateful. The process wasn’t easy, but their team guided me through each step with precision and care. The sense of relief I felt when I regained access to my crypto wallet and saw my funds safely back in place was indescribable. If you find yourself in a similar situation, I highly recommend reaching out to Trust Geeks Hack Expert. contact Them through EMAIL: [email protected] + WEBSITE. HTTPS://TRUSTGEEKSHACKEXPERT.COM + TELE GRAM: TRUSTGEEKSHACKEXPERT

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  • 02.02.25 20:53 Michael9090

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  • 06.02.25 19:42 Marta Golomb

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  • 08.02.25 05:45 [email protected]

    I'm incredibly grateful that I did enough research to recover my stolen cryptocurrency. When I first fell victim to a scam, I felt hopeless and lost, unsure if I'd ever see my funds again. A few months ago, I was approached by someone on Telegram who claimed to have a lucrative investment opportunity in cryptocurrencies. They promised huge returns and played on my emotions, making it seem like a can't-miss chance. I was so eager to make my money grow that I didn't fully vet the situation, and unfortunately, I ended up falling for the scam. They guided me to invest a significant amount of money, and soon after, I realized I had been duped. The scammers blocked me, and my funds were gone. I felt devastated. All of my savings had been wiped out in what seemed like an instant, and the feeling of being taken advantage of was crushing. I spent days researching how to recover my stolen cryptocurrency but found the process to be overwhelming and complicated. I was starting to lose hope when I came across Trust Geeks Hack Expert. At first, I was skeptical about reaching out to a cryptocurrency recovery company, but after reading testimonials and researching their reputation, I decided to give them a try. I contacted Trust Geeks Hack Expert Website: www://trustgeekshackexpert.com/, and I was immediately reassured by their professionalism and expertise. They took the time to listen to my situation, and they were honest about what could and could not be done. What stood out to me was their deep understanding of cryptocurrency fraud and the recovery process. They were able to track down the scammers and initiate the recovery of my stolen funds, step by step. Thanks to Trust Geeks Hack Expert, I was able to get back a significant portion of the cryptocurrency I had lost. Their team was responsive, transparent, and diligent in their efforts. I was kept informed throughout the entire process, and they made sure I felt supported every step of the way. I truly can't thank them enough for their dedication and for restoring my faith in the possibility of recovery after such a devastating loss. I will definitely recommend Trust Geeks Hack Expert to anyone who has fallen victim to a cryptocurrency scam. TeleGram: Trustgeekshackexpert & what's A p p  +1 7 1 9 4 9 2 2 6 9 3

  • 08.02.25 05:46 [email protected]

    I'm incredibly grateful that I did enough research to recover my stolen cryptocurrency. When I first fell victim to a scam, I felt hopeless and lost, unsure if I'd ever see my funds again. A few months ago, I was approached by someone on Telegram who claimed to have a lucrative investment opportunity in cryptocurrencies. They promised huge returns and played on my emotions, making it seem like a can't-miss chance. I was so eager to make my money grow that I didn't fully vet the situation, and unfortunately, I ended up falling for the scam. They guided me to invest a significant amount of money, and soon after, I realized I had been duped. The scammers blocked me, and my funds were gone. I felt devastated. All of my savings had been wiped out in what seemed like an instant, and the feeling of being taken advantage of was crushing. I spent days researching how to recover my stolen cryptocurrency but found the process to be overwhelming and complicated. I was starting to lose hope when I came across Trust Geeks Hack Expert. At first, I was skeptical about reaching out to a cryptocurrency recovery company, but after reading testimonials and researching their reputation, I decided to give them a try. I contacted Trust Geeks Hack Expert Website: www://trustgeekshackexpert.com/, and I was immediately reassured by their professionalism and expertise. They took the time to listen to my situation, and they were honest about what could and could not be done. What stood out to me was their deep understanding of cryptocurrency fraud and the recovery process. They were able to track down the scammers and initiate the recovery of my stolen funds, step by step. Thanks to Trust Geeks Hack Expert, I was able to get back a significant portion of the cryptocurrency I had lost. Their team was responsive, transparent, and diligent in their efforts. I was kept informed throughout the entire process, and they made sure I felt supported every step of the way. I truly can't thank them enough for their dedication and for restoring my faith in the possibility of recovery after such a devastating loss. I will definitely recommend Trust Geeks Hack Expert to anyone who has fallen victim to a cryptocurrency scam. TeleGram: Trustgeekshackexpert & what's A p p  +1 7 1 9 4 9 2 2 6 9 3

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  • 11.02.25 04:24 heyemiliohutchinson

    I invested substantially in Bitcoin, believing it would secure my future. For a while, things seemed to be going well. The market fluctuated, but I was confident my investment would pay off. But catastrophe struck without warning. I lost access to my Bitcoin holdings as a result of several technical issues and inadequate security measures. Every coin in my wallet suddenly disappeared, leaving me with an overpowering sense of grief. The emotional impact of this loss was far greater than I had imagined. I spiraled into despair, feeling as though my dreams of financial independence were crushed. I was on the verge of giving up when I came across Assets_Recovery_Crusader. Being willing to give them a chance, I had nothing left to lose. They listened to my narrative and took the time to comprehend the particulars of my circumstance, rather than treating me like a case number. They worked diligently, using their advanced recovery techniques and deep understanding of blockchain technology to track down my lost Bitcoin. Assets_Recovery_Crusader rebuilt my trust in the bitcoin space. The financial impact had a significant emotional toll, but I was able to get past it thanks to Assets_Recovery_Crusader’s proficiency and persistence. For proper talks, reach out to them via TELEGRAM : Assets_Recovery_Crusader EMAIL: [email protected]

  • 11.02.25 22:46 jimmybrown

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  • 13.02.25 16:50 andytom798

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  • 13.02.25 16:52 birenderkumar20101

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  • 14.02.25 02:56 christophadelbert3

    I was in total dismay when I lost my entire savings investing in cryptocurrency, I was contacted online by a lady through email pretending to be an account manager of a bank, who told me I could make double my savings through cryptocurrency investment, I never imagined it would be a scam and I was going to lose everything. It went on for weeks until I realized that I have been scammed. All hope was lost, I was devastated and broke, fortunately for me, I came across an article on my local bulletin about CYBERPUNK RECOVERY Bitcoin Recovery, I contacted them and provided all the information regarding my case, I was amazed at how quickly they recovered my cryptocurrency funds and was able to trace down those scammers. I’m truly grateful for their service and I recommend CYBERPUNK RECOVERY to everyone who needs to recover their funds urge you to contact CYBERPUNK if you have lost your bitcoin USDT or ETH through bitcoin investment Email: ([email protected]) WhatsApp (+17609237407)

  • 14.02.25 15:33 prelogmilivoj

    I never imagined I would find myself in a situation where I was scammed out of such a significant amount of money, but it happened. I became a victim of a fake online donation project that cost me over $30,000. It all started innocently enough when I was searching for assistance after a devastating fire incident in California. While looking for support, I came across an advertisement that seemed to offer donations for fire victims. The ad appeared legitimate, and I reached out to the project manager to inquire about how to receive the donations. The manager was very convincing and insisted that in order to qualify for the donations, I needed to pay $30,000 upfront. In return, I was promised $1 million in donations. It sounded a bit too good to be true, but in my desperate situation, I made the mistake of believing it. The thought of receiving a substantial amount of help to rebuild after the fire clouded my judgment, and I went ahead and sent the money. However, after transferring the funds, the promised donations never arrived, and the manager disappeared. That’s when I realized I had been scammed. Feeling lost, helpless, and completely betrayed, I tried everything I could to contact the scammer, but all my efforts were in vain. Desperation led me to search for help online, hoping to find a way to recover my money and potentially track down the scammer. That’s when I stumbled upon several testimonies from others who had fallen victim to similar scams and had been helped by a company called Tech Cyber Force Recovery. I reached out to them immediately, providing all the details of the scam and the information I had gathered. To my immense relief, the experts at Tech Cyber Force Recovery acted swiftly. Within just 27 hours, they were able to locate the scammer and initiate the recovery process. Not only did they help me recover the $30,000 I had lost, but the most satisfying part was that the scammer was apprehended by local authorities in their region. Thanks to Tech Cyber Force Recovery, I was able to get my money back and hold the scammer accountable for their actions. I am incredibly grateful for their professionalism, expertise, and dedication to helping victims like me. If you have fallen victim to a scam or fraudulent activity, I highly recommend contacting Tech Cyber Force Recovery. They provide swift and efficient recovery assistance, and I can confidently say they made all the difference in my situation. ☎☎ 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ ☎☎ 📩 1️⃣5️⃣6️⃣1️⃣7️⃣2️⃣6️⃣3️⃣6️⃣9️⃣7️⃣ 📩

  • 14.02.25 22:12 eunice49954

    Agent Jasmine Lopez focuses on recovering stolen cryptocurrency, particularly USDT. She is well-known for helping victims of digital asset theft. Her reputation arises from successful recoveries that have allowed many to regain their lost funds. I witnessed this when $122,000 was taken from me. Thanks to Ms. Lopez's skills, I recovered the entire amount in just 24 hours. Her prompt response and effective methods relieved my financial burden. Ms. Lopez’s commitment to helping others is evident. She is always available to offer solutions to those facing similar problems. For assistance, she can be reached via email at recoveryfundprovider@gmail . com or contacted directly on WhatsApp and text at +44 - 7366 445035. Her Instagram handle is recoveryfundprovider.

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com

  • 15.02.25 02:51 Michelle Lynn

    Living in Los Angeles, I never imagined I’d face such a difficult chapter in my life. At the time, my wife was pregnant, and we were both excited about starting a family. I fell victim to a series of scams, losing over $170,000 in total. Just when I thought things couldn’t get worse, I received a call from someone who promised to help me recover my losses. Desperate to fix the situation, I went along with it, hoping for a breakthrough. But it turned out to be another scam. However, most of the options I found either seemed dubious or offered no real guarantees. That’s when I came across Cyber Constable Intelligence. It was a company recommended in a Facebook community The team worked tirelessly on my case, and after some time, they successfully recovered 99% of my investment. Although I didn’t recover everything, the 99% recovery was a huge relief They also educated me on how to better protect my digital Asset Here's Their Website Info www cyberconstableintelligence com, WhatsApp Info: 1 (252) 378-7611

  • 16.02.25 01:01 Peter

    I fell victim to a crypto scam and lost a significant amount of money. What are the most effective strategies to recover my funds? I've heard about legal actions, contacting authorities, and hiring recovery experts, but I'm not sure where to start. Can you provide some guidance on the best ways to recover money lost in a crypto scam? Well if this is you, [email protected] gat you covered get in touch and thank me later

  • 16.02.25 20:06 eunice49954

    Agent Lopez specializes in recovering stolen cryptocurrencies, especially Bitcoin/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 18.02.25 19:35 donovancristina

    Now, I’m that person sharing my success story on LinkedIn, telling others about the amazing team at TECH CYBER FORCE RECOVERY who literally saved my financial life. I’ve also become that guy who proudly shares advice like “Always back up your wallet, and if you don’t have TECH CYBER FORCE RECOVERY on speed dial.” So, a big thank you to TECH CYBER FORCE RECOVERY if I ever get a chance to meet the team, I might just offer to buy them a drink. They’ve earned it. FOR CRYPTO HIRING WEBSITE WWW://techcyberforcerecovery.com WHATSAPP : ⏩ wa.me/15617263697

  • 18.02.25 22:13 keithphillip671

    WhatsApp +44,7,4,9,3,5,1,3,3,8,5 Telegram @Franciscohack The day my son uncovered the truth—that the man I entrusted my hopes of wealth and companionship with through a cryptocurrency platform was a cunning scammer was the day my world crumbled. The staggering realization that I had been swindled out of 150,000.00 Euro worth of Bitcoin left me in a state of profound despair. As a 73-year-old grappling with loneliness, I had sought solace in what I believed to be a genuine connection, only to find deceit and betrayal. Countless sleepless nights were spent in tears, mourning not only the financial devastation but also the crushing blow to my trust. Attempts to verify the authenticity of our interactions were met with hostility, further deepening my sense of isolation. Through the loss it was my son who became my beacon of resilience. He took upon himself the arduous task of tracing the scam and seeking justice on my behalf. Through meticulous effort and determination, he unearthed {F R A N C I S C O H A C K}, renowned for their expertise in recovering funds lost to cryptocurrency scams. Entrusting them with screenshots and evidence of the fraudulent transactions, my son initiated the journey to reclaim what had been callously taken from me. {F R A N C I S C O H A C K} approached our plight with empathy and unwavering professionalism, immediately instilling a sense of confidence in their abilities. Despite my initial skepticism, their transparent communication and methodical approach reassured us throughout the recovery process. Regular updates on their progress and insights into their strategies provided much-needed reassurance and kept our hopes alive amid the uncertainty. Their commitment to transparency and client welfare was evident in every interaction, fostering a sense of partnership rather than mere service. Miraculously, in what felt like an eternity but was actually an impressively brief period, {F R A N C I S C O H A C K} delivered the astonishing news—I had recovered the entire 150,000.00 Euro worth of stolen Bitcoin. The flood of relief and disbelief was overwhelming, marking not just the restitution of financial losses but the restoration of my faith in justice. {F R A N C I S C O H A C K} proficiency in navigating the intricate landscape of blockchain technology and online fraud was nothing short of extraordinary. Their dedication to securing justice and restoring client confidence set them apart as more than just experts—they were steadfast allies in a fight against digital deceit. What resonated deeply with me {F R A N C I S C O H A C K} integrity and compassion. Despite the monumental recovery, they maintained transparency regarding their fees and ensured fairness in all dealings. Their proactive guidance on cybersecurity measures further underscored their commitment to safeguarding clients from future threats. It was clear that their mission extended beyond recovery—it encompassed education, prevention, and genuine advocacy for those ensnared by cyber fraud. ([email protected]) fills me with profound gratitude. They not only rescued my financial security but also provided invaluable emotional support during a time of profound vulnerability. To anyone navigating the aftermath of cryptocurrency fraud, I wholeheartedly endorse {F R A N C I S C O H A C K}. They epitomize integrity, expertise, and unwavering dedication to their clients' well-being. My experience with {F R A N C I S C O H A C K} transcended mere recovery—it was a transformative journey of resilience, restoration, and renewed hope in the face of adversity.

  • 21.02.25 07:42 daniel231101

    I never thought I would fall victim to a crypto scam until I was convinced of a crypto investment scam that saw me lose all my entire assets worth $487,000 to a crypto investment manager who convinced me I could earn more from my investment. I thought it was all gone for good but I kept looking for ways to get back my stolen crypto assets and finally came across Ethical Hack Recovery, a crypto recovery/spying company that has been very successful in the recovery of crypto for many other victims of crypto scams and people who lost access to their crypto. I’m truly grateful for their help as I was able to recover my stolen crypto assets and get my life back together. I highly recommend their services EMAIL ETHICALHACKERS009 AT @GMAIL DOT COM whatsapp +14106350697

  • 21.02.25 21:38 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 22.02.25 18:01 benluna0991

    Mark Zuckerberg. That’s the name I was introduced to when I first encountered the cryptocurrency mining platform, WHATS Invest. A person claiming to be Zuckerberg himself reached out to me, saying that he was personally backing the platform to help investors like me earn passive income. At first, I was skeptical—after all, how often do you get a direct connection to one of the world’s most famous tech entrepreneurs? But this individual seemed convincing and assured me that many people were already seeing substantial returns on their investments. He promised me a great opportunity to secure my financial future, so I decided to take the plunge and invest $10,000 into WHATS Invest. They told me that I could expect to see significant returns in just a few months, with payouts of at least $1,500 or more each month. I was excited, believing this would be my way out of financial struggles. However, as time passed, things didn’t go according to plan. Months went by, and I received very little communication. When I finally did receive a payout, it was nowhere near the $1,500 I was promised. Instead, I received just $200, barely 13% of what I had expected. Frustrated, I contacted the support team, but the responses were vague and unhelpful. No clear answers or solutions were offered, and my trust in the platform quickly started to erode. It became painfully clear that I wasn’t going to get anywhere with WHATS Invest, and I began to worry that my $10,000 might be lost for good. That's when I discovered Certified Recovery Services. Desperate to recover my funds, I decided to reach out to them for help. In just 24 hours, they worked tirelessly to recover the majority of my funds, successfully retrieving $8,500 85% of my initial investment. I couldn’t believe how quickly and efficiently they worked to get my money back. I’m extremely grateful for Certified Recovery Servicer's fast and professional service. Without them, I would have been left with a significant loss, and I would have had no idea how to move forward. If you find yourself in a similar situation with WHATS Invest or any other platform that isn’t delivering as promised, I highly recommend reaching out to Certified Recovery Services They were a lifesaver for me, helping me recover nearly all of my funds. It's reassuring to know that trustworthy services like this exist to help people when things go wrong. They also specialize in recovering money lost to online scams, so if you’ve fallen victim to such a scam, don’t hesitate to contact Certified Recovery Services they can help! Here's Their Info Below: WhatsApp: +1(740)258‑1417 mail: [email protected], [email protected] Website info; https://certifiedrecoveryservices.com

  • 23.02.25 22:00 eunice49954

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 06:36 ANDREW DAVIS

    RECOVER YOUR SCAMMED FUNDS AND CRYPTOCURRENCY VIA SPOTLIGHT RECOVERY Professional hackers at Spotlight Recovery provide services for compromised devices, accounts, and websites as well as for recovering stolen bitcoin and money from scams. They finish their work safely and quickly. Their order has been fulfilled since day one, and the victim will never be conscious of the outside entrance. Very few even attempt to give critical information, look into network security, or discreetly discuss personal issues. The Spotlight Recovery Crew helped me recover $264,000 that was stolen from my corporate bitcoin wallet, and I appreciate them giving me further details on the unidentified people. In the event that you have been defrauded of your hard-earned cash or bitcoins, contact SPOTLIGHT RECOVERY CREW at Contact: [email protected]

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 24.02.25 07:19 maggie4567

    Jasmine Lopez specializes in recovering stolen cryptocurrencies, especially ETH/USDT. She has built a strong reputation for helping victims reclaim their lost funds. A personal example highlights her effectiveness: I lost $111,000 and, thanks to her prompt action, I recovered it all within 24 hours. Her dedication and skills eased my financial stress. She is always ready to assist others with similar issues. For help, she can be reached by email at Recoveryfundprovider@gmail. com or contact her through WhatsApp at +44 736 644 5035. Her Insta is recoveryfundprovider.

  • 27.02.25 12:46 monikaguttmacher

    Weddings are supposed to be magical, but the months leading up to mine were anything but. Already, wedding planning was a high-stress, sleep-deprived whirlwind: endless details to manage, from venue deposits and guest lists to dress fittings and vendor contracts. But nothing-and I mean, nothing-compared to the panic that washed over me when I realized that somehow, I had lost access to my Bitcoin wallet-with $600,000 inside. It happened in the worst possible way. In between juggling my to-do lists and trying to keep my sanity intact, I lost my seed phrase. I went through my apartment like a tornado, flipping through notebooks, checking every email, every file-nothing. I sat there in stunned silence, heart pounding, trying to process the fact that my entire savings, my security, and my financial future might have just vanished. In utter despair, I vented to my bridesmaid's group chat for some sympathetic words from the girls. Instead, one casually threw out a name that would change everything in a second: "Have you ever heard of Tech Cyber Force Recovery? They recovered Bitcoin for my cousin. You should call them." I had never heard of them before, but at that moment, I would have tried anything. I immediately looked them up, scoured reviews, and found story after story of people just like me—people who thought they had lost everything, only for Tech Cyber Force Recovery to pull off the impossible. That was all the convincing I needed. From the very first call, I knew I was in good hands. Their team was calm, professional, and incredibly knowledgeable. They explained the recovery process in a way that made sense, even through my stress-fogged brain. Every step of the way, they kept me informed, reassured me, and made me feel like this nightmare actually had a solution. And then, just a few days later, I got the message: "We have recovered your Bitcoin." (EMAIL. support @ tech cyber force recovery . com) OR WHATSAPP (+1 56 17 26 36 97) I could hardly believe my eyes: Six. Hundred. Thousand. Dollars. In my hands again. I let out my longest breath ever and almost cried, relieved. It felt like I woke up from a bad dream, but it was real, and Tech Cyber Force Recovery had done it. Because of them, I walked down the aisle not just as a bride, but as someone who had dodged financial catastrophe. Instead of spending my honeymoon stressing over lost funds, I got to actually enjoy it—knowing that my wallet, and my future, were secure. Would I refer to them? In a heartbeat. If you ever find yourself in that situation, please don't freak out, just call Tech Cyber Force Recovery. They really are the real deal.

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  • 07.03.25 15:18 Ariduk

    BEST LINK FOR RECOVERY SCAM ON TRADING INVESTMENT AND OTHERS TROUBLESHOOT?  Welcome To General Hacking Techniques Service known as DHACKERS.  Year 2025 we are active and best in what we do, as we give Solution to every problem concerning Web3 INTERNET activities, we guide you right to a positive fund Recovery e.t.c. Question From Most of Our Client, HOW POSSIBLE AND TIME WILL IT TAKE TO RECOVERY LOST OR SCAM  FUND? Our Answer.  Yes is 89.9% possible and how long it takes your Fund to be recovered depend on you.  The fact is there are lot of fake binary investment companies out their, same a lot fake recovery companies and agents too.                   CAUTION 1). Make sure you ask one or two questions concerning the service and how they render there recovery services. 2) Do not give out your scammed details to any agent or hacker when you are not yet ready to recover back your fund. 3) do not make any payment when you are not sure of the service if you are to make one. (4) We discovered that most of this fake hacker or agent do give us scam details, playing the victim, because they can afford the service charge. 5) As long you have your scam details with you, you can recover your fund back anytime any-day with the right channel. Contact us from our Front Desk. [email protected] For advice and services, our standby Guru email. [email protected] [email protected]  List of Service. ▶️Binary Recovery ▶️Data Recovery  ▶️University Result Upgraded ▶️Clear your Internet Blunder and controversy  ➡️Increase your Credit Score  ➡️Wiping of Criminal Records  ➡️Social Media Hack  ➡️Blank ATM Card  ➡️Load and wipe ➡️Phone Hacking ➡️Private Key Reset etc.  For quick response. Email [email protected]  Border us with your jobs and allow us give you positive result with our hacking skills.  All Right Reserved (c)2025

  • 08.03.25 04:42 andreassenhedda

    If you’ve fallen victim to online scammers who have deceived you into investing your hard-earned money through fraudulent Bitcoin schemes, know that you're not alone. Countless individuals have been misled by scammers using various tactics to swindle money through deceptive investment platforms or promises of high returns. These scams often come in the form of fake cryptocurrency investments, Ponzi schemes, or phishing scams designed to steal your Bitcoin and other assets. Unfortunately, many victims suffer not only financial loss but also the psychological toll of realizing they’ve been taken advantage of. In some cases, scammers go even further, threatening legal consequences or using intimidation to keep victims silent. However, all hope is not lost. There are ways to recover your funds and potentially even bring those responsible to justice. One promising resource that can assist in reclaiming lost funds is Lee Ultimate Hacker, a trusted platform designed to help victims of online fraud. This service specializes in helping individuals who have been scammed through cryptocurrency investments or similar schemes. The platform’s expertise can help you navigate the process of recovering your money, giving you a chance to fight back against those who’ve wronged you. To begin the recovery process, it’s important to gather any proof of payment or transaction history involving the scammers. This evidence is crucial in tracing the flow of funds and identifying the scam operation behind it. Once you have collected your documentation, you can reach out to Lee Ultimate Hacker via LEEULTIMATEHACKER @ AOL . COM or wh@tsapp +1 (715) 314 - 9248 for assistance. They offer professional services to investigate fraudulent schemes, track Bitcoin transactions, and work to reverse fraudulent transfers. The recovery process can be complex and requires expert knowledge of blockchain technology and financial investigations, but with the help of a dedicated team, you’ll have a better chance of seeing your funds returned. In addition to helping you recover your assets, Lee Ultimate Hacker also works towards identifying the scammers and reporting them to the appropriate authorities. They understand the urgency of halting these fraudsters before they can victimize others. With their assistance, you not only increase the chances of recovering your funds but also play a part in holding cybercriminals accountable. If you've been affected by Bitcoin scams or other fraudulent online activities, reaching out to a service like Lee Ultimate Hacker can provide hope and a clear path forward. Their team can guide you through the recovery process, offering expert support while you take steps to reclaim what you’ve lost. It’s time to take action and work toward reclaiming your hard-earned money.

  • 08.03.25 18:20 Diegolola514gmail.com

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  • 08.03.25 18:23 faridasumadi

    WEBSITE W.W.W.techcyberforcerecovery.com WHATSAPP +1 561.726.36.97 EMAIL [email protected] I Thought I Was Too Smart to Be Scammed, Until I Was. I'm an attorney, so precision and caution are second nature to me. My life is one of airtight contracts and triple-checking every single detail. I'm the one people come to for counsel. But none of that counted for anything on the day I lost $750,000 in Bitcoin to a scam. It started with what seemed like a normal email, polished, professional, with the same logo as my cryptocurrency exchange's support team. I was between client meetings, juggling calls and drafting agreements, when it arrived. The email warned of "suspicious activity" on my account. My heart pounding, I reacted reflexively. I clicked on the link. I entered my login credentials. I verified my wallet address. The reality hit me like a blow to the chest. My balance was zero seconds later. The screen went dim as horror roiled in my stomach. The Bitcoin I had worked so hard to accumulate over the years, stored for my retirement and my children's future, was gone. I felt embarrassed. Lawyers are supposed to outwit criminals, not get preyed on by them. Mortified, I asked a client, a cybersecurity specialist, for advice, expecting criticism. But he just suggested TECH CYBER FORCE RECOVERY. He assured me that they dealt with delicate situations like mine. I was confident from the first call that I was in good hands. They treated me with empathy and discretion by their staff, no patronizing lectures. They understood the sensitive nature of my business and assured me of complete confidentiality. Their forensic experts dove into blockchain analysis with attention to detail that rivaled my own legal work. They tracked the stolen money through a complex network of offshore wallets and cryptocurrency tumblers tech jargon that appeared right out of a spy thriller. Once they had identified the thieves, they initiated a blockchain reversal process, a cutting-edge method I was not even aware was possible. Three weeks of suffering later, my Bitcoin was back. Every Satoshi counted for. I sat in front of my desk, looking at the refilled balance, tears withheld. TECH CYBER FORCE RECOVERY not only restored my assets, they provided legal-grade documentation that empowered me to bring charges against the scammers. Today, I share my story with colleagues as a warning. Even the best minds get it. But when they do, it is nice to know the Wizards have your back.

  • 09.03.25 17:22 Wayne707

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  • 10.03.25 18:18 springwilli

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